[House Hearing, 118 Congress]
[From the U.S. Government Publishing Office]


                    ADVANCES IN DEEPFAKE TECHNOLOGY

=======================================================================

                                HEARING

                               BEFORE THE

               SUBCOMMITTEE ON CYBERSECURITY, INFORMATION
                 TECHNOLOGY, AND GOVERNMENT INNOVATION

                                 OF THE

                         COMMITTEE ON OVERSIGHT
                           AND ACCOUNTABILITY

                        HOUSE OF REPRESENTATIVES

                    ONE HUNDRED EIGHTEENTH CONGRESS

                             FIRST SESSION

                               __________

                            NOVEMBER 8, 2023

                               __________

                           Serial No. 118-74

                               __________

  Printed for the use of the Committee on Oversight and Accountability
  
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]  


                       Available on: govinfo.gov
                         oversight.house.gov or
                             docs.house.gov
                             
                             
                               __________

                                
                    U.S. GOVERNMENT PUBLISHING OFFICE                    
54-071 PDF                     WASHINGTON : 2024                    
          
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               COMMITTEE ON OVERSIGHT AND ACCOUNTABILITY

                    JAMES COMER, Kentucky, Chairman

Jim Jordan, Ohio                     Jamie Raskin, Maryland, Ranking 
Mike Turner, Ohio                        Minority Member
Paul Gosar, Arizona                  Eleanor Holmes Norton, District of 
Virginia Foxx, North Carolina            Columbia
Glenn Grothman, Wisconsin            Stephen F. Lynch, Massachusetts
Gary Palmer, Alabama                 Gerald E. Connolly, Virginia
Clay Higgins, Louisiana              Raja Krishnamoorthi, Illinois
Pete Sessions, Texas                 Ro Khanna, California
Andy Biggs, Arizona                  Kweisi Mfume, Maryland
Nancy Mace, South Carolina           Alexandria Ocasio-Cortez, New York
Jake LaTurner, Kansas                Katie Porter, California
Pat Fallon, Texas                    Cori Bush, Missouri
Byron Donalds, Florida               Jimmy Gomez, California
Kelly Armstrong, North Dakota        Shontel Brown, Ohio
Scott Perry, Pennsylvania            Melanie Stansbury, New Mexico
William Timmons, South Carolina      Robert Garcia, California
Tim Burchett, Tennessee              Maxwell Frost, Florida
Marjorie Taylor Greene, Georgia      Summer Lee, Pennsylvania
Lisa McClain, Michigan               Greg Casar, Texas
Lauren Boebert, Colorado             Jasmine Crockett, Texas
Russell Fry, South Carolina          Dan Goldman, New York
Anna Paulina Luna, Florida           Jared Moskowitz, Florida
Chuck Edwards, North Carolina        Rashida Tlaib, Michigan
Nick Langworthy, New York
Eric Burlison, Missouri

                                 ------                                
                       Mark Marin, Staff Director
       Jessica Donlon, Deputy Staff Director and General Counsel
             Raj Bharwani, Senior Professional Staff Member
                 Lauren Lombardo, Senior Policy Analyst
                      Peter Warren, Senior Advisor
      Mallory Cogar, Deputy Director of Operations and Chief Clerk

                      Contact Number: 202-225-5074

                  Julie Tagen, Minority Staff Director

                      Contact Number: 202-225-5051
                                 ------                                

 Subcommittee on Cybersecurity, Information Technology, and Government 
                               Innovation

                 Nancy Mace, South Carolina, Chairwoman
William Timmons, South Carolina      Gerald E. Connolly, Virginia 
Tim Burchett, Tennessee                  Ranking Minority Member
Marjorie Taylor Greene, Georgia      Ro Khanna, California
Anna Paulina Luna, Florida           Stephen F. Lynch, Massachusetts
Chuck Edwards, North Carolina        Kweisi Mfume, Maryland
Nick Langworthy, New York            Jimmy Gomez, California
Eric Burlison, Missouri              Jared Moskowitz, Florida
Vacancy                              Vacancy
                        
                        C  O  N  T  E  N  T  S

                              ----------                              

                                                                   Page

Hearing held on November 8, 2023.................................     1

                               Witnesses

                              ----------                              

Mr. Mounir Ibrahim, Vice President of Public Affairs and Impact, 
  Truepic
Oral Statement...................................................     5
Dr. David Doermann, Interim Chair, Computer Science and 
  Engineering, State University of New York at Buffalo
Oral Statement...................................................     7
Mr. Sam Gregory, Executive Director, WITNESS
Oral Statement...................................................     8
Mr. Spencer Overton (Minority Witness), Professor of Law, George 
  Washington University School of Law
Oral Statement...................................................    10

Written opening statements and statements for the witnesses are 
  available on the U.S. House of Representatives Document 
  Repository at: docs.house.gov.

                           Index of Documents

                              ----------                              

  * Questions for the Record: to Mr. Ibrahim; submitted by Rep. 
  Connolly.

Documents are available at: docs.house.gov.

 
                    ADVANCES IN DEEPFAKE TECHNOLOGY

                              ----------                              


                      Wednesday, November 8, 2023

                        House of Representatives

               Committee on Oversight and Accountability

 Subcommittee on Cybersecurity, Information Technology, and Government 
                               Innovation

                                           Washington, D.C.

    The Subcommittee met, pursuant to notice, at 3:41 p.m., in 
room 2154, Rayburn House Office Building, Hon. Nancy Mace 
[Chairwoman of the Subcommittee] presiding.
    Present: Representatives Mace, Timmons, Burchett, 
Langworthy, Burlison, and Connolly.
    Ms. Mace. Good afternoon, everyone. The Subcommittee on 
Cybersecurity, Information Technology, and Government 
Innovation will come to order.
    Welcome, everyone, and without objection, the Chair may 
declare a recess at any time.
    I recognize myself for the purpose of making an opening 
statement.
    Good afternoon, and welcome to this hearing of the 
Subcommittee on Cybersecurity, Information Technology, and 
Government Innovation.
    The groundbreaking power of artificial intelligence is a 
double-edged sword. That is nowhere more evident than in AI's 
capacity to generate realistic-looking images, audio, and 
video. The latest AI algorithms can be used to make synthetic 
creations nearly indistinguishable from actual faces, voices, 
and events. These creations are referred to as deepfakes.
    Deepfakes can be put to a variety of productive uses. They 
are used to enhance video games and other forms of 
entertainment, and they are being used to advance medical 
research as well, but deepfake technology can also be 
weaponized and cause great harm. It can be used to make people 
appear to say or do things that they have not said or done. It 
can be used to perpetuate various crimes, including financial 
fraud and intellectual property theft. It can also be used by 
anti-American actors to create national security threats, and 
these are not hypothetical harms.
    It must be me today.
    OK. A few weeks ago, AI-generated pornographic images of 
female students at a New Jersey high school were circulated by 
male classmates. A company that studies deepfakes found more 
than 90 percent of deepfake images are pornographic. Last 
month, the attorneys general of 54 states and territories wrote 
to congressional leaders urging they address how AI is being 
used to exploit children, specifically through the generation 
of child sexual abuse material, or CSAM, pronounced ``Cee-
Sam.'' They wrote, ``AI can combine data from photographs of 
both abused and non-abused children to animate new and 
realistic sexualized images of children who do not exist but 
may resemble actual children. Creating these images is easier 
than ever,'' the letter states, ``as anyone can download the AI 
tools to their computer and create images by simply typing in a 
short description of what the user wants to see.''
    Falsified videos and photos circulating on social media are 
also making it difficult to separate fact from fiction in 
conflicts taking place around the world. Videos purportedly 
taken from the ground in Israel, Gaza, and Ukraine have 
circulated rapidly around on social media, only to be proven 
inauthentic. One AI-generated clip showed the President of 
Ukraine urging troops to put down their arms. I am not 
interested in banning all synthetic images or videos that 
offend some people or make them feel uncomfortable, but if we 
cannot separate truth from fiction, we cannot ensure our laws 
are enforced or that our national security is preserved. And 
there is more insidious danger that the sheer volume of 
impersonations and false images we are exposed to on social 
media lead us to no longer recognize reality when it is staring 
us right in the face.
    Bad actors are rewarded when people think everything is 
fake, thus called the liar's dividend. The classic case of the 
liar's dividend is the very real hunter Biden laptop, which 
many in the media and elsewhere falsely attributed to Russian 
disinformation, but the risk from deepfakes can be mitigated. 
We will hear about one such effort today being pursued by a 
partnership of tech companies interested in maintaining a flow 
of trusted content. Voluntary standards can enable creators to 
embed content provenance data into an image or video, allowing 
others to know if the content is computer generated or has been 
manipulated in any way.
    Our witnesses today will be able to discuss these 
standards, along with other ideas, for addressing the potential 
harms caused by deepfakes. With that, I would like to yield to 
the Ranking Member of the Subcommittee, my friend, Mr. 
Connolly.
    Mr. Connolly. Thank you, Madam Chairwoman, and thank you 
for having this hearing. Very timely. I will begin by noting 
our disappointment on the Minority side, yet again, the lack of 
a hearing on the scorecard for implementation of FITARA. This 
Committee initiated that legislation, created that scorecard, 
has had 15 hearings--a record for Congress--and it has produced 
over $25 billion of savings. We believe strongly that we need 
to continue that oversight and continue to press the executive 
branch for progress. I would note that until now, that effort 
over the last 7 years has been completely bipartisan. I have 
worked with my colleagues on the other side of the aisle--Mr. 
Meadows, Mr. Issa, Mr. Hice, Mr. Hurd--to make this happen, and 
we have always collaborated in a bipartisan way to make it 
happen. So, I hope we can revisit that issue and continue to 
make progress and keep what I think is a very proud record by 
this Subcommittee and by the full Committee in holding the 
executive branch's feet to the fire when it comes to IT 
modernization, updating cybersecurity encryption, and moving to 
the cloud.
    [Slides shown]
    Mr. Connolly. With that, with respect to this hearing, when 
most people hear the term ``deepfake,'' this image may jump to 
mind. While images like this, His Holiness, Pope Francis, in a 
puffy coat, seems innocuous, most are, as the Chairwoman just 
indicated, quite insidious. Take the AI-generated image, for 
example, the Gaza image on the screen. Since the armed conflict 
between Israel and Hamas first broke out, false images created 
by generative technology have proliferated throughout the 
internet. As a result, these synthetic images have created an 
algorithmically driven fog of war, making it significantly more 
difficult to differentiate between truth and fiction. Just last 
year, at the outset of Russia's invasion of Ukraine, a 
fabricated video of Ukrainian President Zelensky calling for 
Ukrainian soldiers to lay down their weapons, also referenced 
by the Chairwoman, circulated on social media very widely. It 
was a deepfake, but thanks to Ukraine's quick response to 
Russian disinformation, it was quickly debunked. Welcome to the 
new frontier of disinformation.
    Politics are one realm of deepfakes, but let us look at 
some numbers. According to one study, 96 percent of deepfake 
videos are of non-consensual pornography--96 percent. Another 
report confirmed that deepfake pornography almost exclusively 
targets and harms young women. Knowing this, it should be no 
surprise that the very first deepfake ever created depicted the 
face of a famous female celebrity superimposed onto the body of 
an actor in a pornographic video. These kinds of manipulated 
videos are already affecting students in our schools. In one 
instance, a group of high school students in New Jersey used 
the images of a dozen female classmates to make AI pornography. 
This is wrong. It threatens lives and self-esteem among young 
people, and it needs to be stopped.
    Earlier this year, Ranking Member, Joe Morelle, introduced 
a bill called the Preventing Deepfakes of Intimate Images Act. 
The bill bans non-consensual images. The order instructs the 
Secretary of Commerce--whoops, I am sorry--of sharing synthetic 
intimate images and creates additional legal courses of action 
for those who are affected. I am a co-sponsor of that 
legislation and urge all of my colleagues to join us in this 
important effort.
    Congress must not shy away from preventing harmful 
proliferation of deepfake pornography, but it is not just 
deepfake videos that we have to worry about. With AI, scammers 
have the ability to easily create audio that mimics a person's 
voice, matching, age, gender, and tone. Thousands of Americans 
are scammed over the telephone every year using this very 
technology, and deepfake capabilities further exacerbate the 
problem. So, what can we do? AI image detecting tools are being 
developed and used to help verify the authenticity of machine-
generated images. Other tools place watermarks on AI-generated 
media to indicate that the media is synthetically created.
    While these tools improve and evolve, both the public and 
the private sector must cooperate to educate the public on 
where these tools are and how to use them. Government and the 
private sector must collaboratively highlight the dangers and 
consequences of deepfakes and teach how to combat this 
misinformation and its abuse. Private developers must implement 
policies that preserve the integrity of truth and to provide 
transparency to users. That is why I joined the letter, led by 
Representative Kilmer and the New Democratic Coalition AI 
Working Group, that requests leaders of prominent, generative 
AI and social media platforms to provide information to 
Congress outlining efforts to monitor, identify, and disclose 
deceptive synthetic media content, and the public sector is 
already taking bold, consequential steps toward collaboration 
and comprehensive solutions.
    I applaud the efforts of the Biden Administration to secure 
commitments from seven major AI companies to help users 
identify when content is, in fact, AI generated and when it is 
not. The Biden Administration took a resolute and unprecedented 
step last week when it issued its executive order on artificial 
intelligence. The sweeping executive order speaks directly to 
the issues we seek to examine today. It leans on tools, like 
watermarking, that can help people identify whether what they 
are looking at online is authentic as a government document or 
tool of disinformation. The order instructs the Secretary of 
Commerce to work enterprise-wide to develop standards and best 
practices for detecting fake content and tracking the 
provenance of authentic information. I trust this Subcommittee 
will conduct meaningful oversight of these efforts because we, 
as a Nation, need to get this right.
    I am proud that the Administration has taken the first step 
in performing its role as a global leader in addressing 
generative technology. I also look forward to hearing more 
today about existing and evolving private sector solutions and 
suggestions. We already know Congress must continue to fund 
essential research programs that support the development of 
more advanced and effective deepfake detection tools. Funding 
for research through DARPA and the National Science Foundation 
is critical. That requires a fully funded government. I once 
again urge all of my colleagues on this and the other side of 
the aisle to fulfill our constitutional duty and work with us 
to pass a bipartisan, long-term funding agreement.
    I thank the Chairwoman, Ms. Mace, for holding this hearing 
and emphasizing the harm of deepfakes and disinformation, and I 
look forward to any legislative action that may follow this 
endeavor. I yield back.
    Ms. Mace. Thank you, Mr. Connolly. Today, I am pleased to 
introduce our witnesses for today's hearing. Our first witness 
is Mr. Mounir Ibrahim, Executive Vice President of Public 
Affairs and Impact at Truepic. I would like to next introduce 
Mr. Langworthy to introduce the second witness.
    Mr. Langworthy. Thank you very much, Madam Chair. I am 
pleased to have the opportunity to introduce our witness from 
Western New York. Dr. David Doermann is the interim Chair of 
the Department of Computer Science and Engineering at the State 
University of New York at Buffalo. He is also a professor of 
empire innovation and the Director of the Artificial 
Intelligence Institute at UB. Prior to UB, Dr. Doermann was a 
program manager at the Defense Advanced Research Projects 
Agency, or DARPA, where he developed and oversaw $150,000,000 
in research funding in the areas of computer vision, human 
language, and voice analytics technologies. Dr. Doermann is a 
leading researcher and innovative thinker in the areas of 
document image analysis and recognition. Welcome to the 
hearing, Dr. Doermann. We look forward to your testimony today, 
and I yield back.
    Ms. Mace. Thank you. Our third witness is Mr. Sam Gregory, 
executive director of WITNESS, and our fourth witness today is 
Mr. Spencer Overton, professor of law at George Washington 
University School of Law. Welcome, everyone, and we are pleased 
to have you here this afternoon.
    Pursuant to Committee Rule 9(g), the witnesses will please 
stand and raise your right hands.
    Do you solemnly swear or affirm that the testimony you are 
about to give is the truth, the whole truth, and nothing but 
the truth, so help you God?
    [A chorus of ayes.]
    Ms. Mace. Let the record show the witnesses all answered in 
the affirmative. We appreciate all of you being here today and 
look forward to your testimony.
    Let me remind the witnesses that we have read your written 
statements, and they will appear in full in the hearing record. 
Please limit your oral statements to 5 minutes. As a reminder, 
please press the button on the microphone in front of you so 
that it is on, and the Members can hear you. When you begin to 
speak, the light in front of you will turn green. After 4 
minutes, the light will turn yellow. When the red light comes 
up, your 5 minutes has expired, and we would ask that you 
please wrap it up.
    I would like to recognize Mr. Ibrahim to please begin his 
opening statement.

                      STATEMENT OF MOUNIR IBRAHIM

              VICE PRESIDENT OF PUBLIC AFFAIRS AND IMPACT

                                TRUEPIC

    Mr. Ibrahim. Thank you, Chairwoman Mace, Ranking Member 
Connolly, and Members of this Subcommittee for the opportunity 
to brief today. My name is Mounir Ibrahim, Executive Vice 
President for Truepic, a technology company that focuses on 
digital content transparency and authenticity. Prior to my time 
at Truepic, I was a Foreign Service officer with the U.S. 
Department of State. My time as a U.S. diplomat was one of the 
greatest honors of my life and led me to my work today.
    I was posted at the U.S. Embassy in Damascus at the start 
of the Arab Spring. I saw protesters risk their lives, being 
beaten and attacked in front of me, as they attempted to 
document the violence with smartphones. It was there I saw the 
power of user-generated content. Later, as an advisor to two 
different U.S. permanent representatives to the United Nations, 
I saw similar images from conflict zones around the world enter 
into the U.N. Security Council, but regularly undermined by 
countries, critics, and bad actors that wanted to undermine 
reality by simply claiming those images were fake. Today, that 
strategy, as the Congresswoman noted, is referred to as the 
liar's dividend, and it is highly effective, simply claiming 
user-generated content is fake.
    In my opinion, in addition to the horrible, non-consensual 
pornographic threat that we have from generative AI, the liar's 
dividend is one of the biggest challenges we have because we 
have digitized our entire existence. Government, business, and 
people all rely on what we see and hear online to make 
decisions. We need transparency and authenticity in that 
content to make accurate decisions. There is no silver bullet 
to transparency or authenticity online, but there is growing 
consensus that adding transparency to digital content so that 
content consumers--the people who are seeing those images and 
videos--can tell what is authentic, what is edited, or what is 
synthetic, will help mitigate the challenges.
    A lot of this work is taking place by a coalition of 
organizations known as the Coalition for Content Provenance and 
Authenticity, C2PA, by which Truepic is a proud member. The 
C2PA developed the world's first open standard for digital 
content provenance, which is often referred to as content 
credentials. The basic concept of provenance is attaching the 
fact or history of that image, that video, that audio that you 
are interacting with online to the file itself, so the content 
consumer is informed of the artifacts associated with that, 
like time, date, how it was created, how it was edited, et 
cetera. The standard is interoperable, which is critical, so 
digital content can flow from one platform, one device to 
another so as long as they align to the same standard.
    Truepic supports the C2PA because we believe 
interoperability is critical to help mitigate the challenges 
that you all laid out today. Our technology and our approach 
boils down into two areas: we help secure what is, in fact, 
authentic and captured from a smartphone, and we help add 
transparency to a synthetic or generative piece that is created 
from a platform.
    From the authentic side, we created a technology called 
Secure Controlled Capture. It is used by hundreds of businesses 
every day, ranging from Equifax to Ford Motor Company, to add 
transparency into their operations. It has also been used in 
150 countries. We have deployed this technology on the ground 
in Ukraine with Microsoft to help USAID partners document 
destruction to cultural heritage and national infrastructure. 
On the generative AI side, the C2PA standard has been 
recognized by the Partnership for AI as one of the lead 
disclosure mechanisms for generative AI. We are also a proud 
supporter of the Partnership for AI, and we strive to work 
toward this goal.
    In April, we worked with Nina Schick, an author, and 
Revel.ai, to launch the world's first transparent deepfake so 
when you actually see the video, you see the content 
credentials and know it is, in fact, generated by AI. This past 
month, we launched two other partnerships with Hugging Face to 
democratize these tools to add content credentials so anyone 
can use them on their open-source models, and with Qualcomm, 
the chipset manufacturer. We think that is a watershed 
innovation because generative AI is going to move to your 
smartphone, and this chipset has this transparency technology 
added to it. It is worth noting that it is not only Truepic 
working on this. Microsoft, Adobe, Stability AI have all either 
launched products or made commitments to add the same 
transparency. This is a growing ecosystem.
    In closing, if possible, I would like to offer some 
thoughts on how government might help mitigate these AI 
challenges with transparency and authenticity in mind. First, 
government has a unique platform, and I applaud you for having 
this hearing. Events like this will raise awareness and help 
educate the public and give an opportunity to ask the right 
questions. Second, legislation can be powerful. We have seen in 
the National Defense Authorization Act, the bipartisan Deepfake 
Task Force Act, and the recent Executive Order Section 4.5, all 
of which point to transparency and authenticity in digital 
content. We have also seen it abroad in the U.K. and the EU. 
Finally, government should consider how it can use content 
credentials to authenticate its own communications and prevent 
constituents from being deceived, and also reap the same 
benefits that the private sector does in cost reductions, risk 
reductions, and fraud reductions.
    Thank you for your time, and I welcome any questions.
    Ms. Mace. Thank you. I now recognize Mr. Doermann to please 
begin his opening statement.

                    STATEMENT OF DR. DAVID DOERMANN

                             INTERIM CHAIR

                    COMPUTER SCIENCE AND ENGINEERING

                STATE UNIVERSITY OF NEW YORK AT BUFFALO

    Mr. Doermann. Chairwoman Mace, Ranking Member Connolly, and 
honorable Members of Congress, I appreciate the opportunity to 
testify before you today on the pressing issue of deepfake 
technology, creating and distributing computer-generated 
images, and voice cloning.
    In 2014, only a decade ago, when DARPA began the Media 
Forensics Program, commonly known as MediFor, the primary goal 
was to detect and characterize image manipulation at scale. 
This was consistent with DARPA's mission of preventing 
strategic surprise. Although we imagined a world where our 
adversaries would become better at manipulating images, few 
imagined the pace at which automated manipulation would develop 
and the impact the technology would have on our society as a 
whole. The introduction of generative adversarial networks, or 
GANs, kicked off a plethora of tools that can generate images 
of people and objects that do not exist, synthesize speech that 
clones voices of others, implements real-time puppeteering to 
control talking heads, and, as we hear most, the ability to 
generate deepfake videos. The surprise we missed perhaps is the 
automated tools that are becoming more accessible and user 
friendly. They require a lot less data and a lot less technical 
expertise. Open-source software can be downloaded today and run 
by any one of us on a commodity laptop.
    As these technologies advance at an unprecedented rate, it 
is crucial to recognize the potential for both positive and 
negative implications. We hear its use at both ends of the 
spectrum every week. This week we heard about AI being used to 
finish a new Beatles song and, as we have heard from the 
Ranking Member and Chairwoman, that a group of students in a 
New Jersey high school used it to generate pornographic videos 
of their classmates. Despite the President's executive order 
and the testimony of our thought leaders and business leaders, 
we are not moving fast enough to curtail the continued damage 
this technology is doing and will do as it evolves. Not only 
has it been used for non-consensual pornography, cyberbullying, 
and harassment, causing great harm to individuals, but the 
potential for national security implications are grave. 
Deepfakes can be used to impersonate government officials, 
military personnel, or law enforcement, and, in general, lead 
to misinformation and potentially dangerous situations.
    Today, this is no longer a problem that can be solved by 
simply detecting and removing generated content from our social 
media and content provider sites. I urge you to consider 
legislation and regulation to address the misuse of deepfake 
technology as a whole.
    Striking the right balance between free speech and 
safeguards to protect against malicious uses of deepfakes is 
essential. First and foremost, public awareness and digital 
literacy programs are vital to helping individuals learn about 
the existence of deepfakes and how to ensure that they do not 
propagate this type of misinformation. It may seem obvious that 
people want to know what type of information is being 
generated, and you would hope that they would be able to hold 
it upon themselves not to spread it, but we find that that is 
not the case. We should consider including school media 
literacy education and promote critical thinking.
    Collaboration between Congress and technology companies is 
essential, and I am glad to see that that is happening to 
address the challenges posed by deepfakes. Tech companies 
should be responsible for developing and implementing the 
policies to detect and mitigate this type of content, including 
what we were hearing today, on their platforms and sharing, 
most importantly, what they learn with others. We have 
addressed this type of a problem with our cybersecurity, and we 
should be doing that same thing with our misinformation. More 
robust privacy and consent laws are needed to protect 
individuals from using their likeness and voice in deepfake 
content without their permission, and continued research and 
development in AI deepfake technology are necessary, as is 
funding to counter deepfake misuse.
    We have created these problems, but I have no doubt that if 
we work together, we are smart enough to figure out how to 
solve them. I look forward to taking your questions.
    Ms. Mace. Thank you. I now recognize Mr. Gregory to please 
begin his opening statement.

                        STATEMENT OF SAM GREGORY

                           EXECUTIVE DIRECTOR

                                WITNESS

    Mr. Gregory. Chairwoman Mace, Ranking Member Connolly, and 
Members of the Subcommittee, I am Sam Gregory, Executive 
Director of the human rights organization, WITNESS. Since 2018, 
WITNESS has led a global effort--Prepare, Don't Panic--to 
inclusively prepare for deepfake and related synthetic media 
and generative AI technologies. The capabilities and uses of 
deepfakes have often been overhyped, but with recent shifts, 
the moment to address them more comprehensively has come. 
First, I will cover technological advances.
    Commercialization and accessibility characterized changes 
in the last year. Deepfake technologies are now available in 
widely used consumer tools, not just niche apps. Furthermore, 
they are easy to use and, particularly for audio and image, can 
be instructed with plain language and require no coding skills. 
Realistic image generation has improved dramatically in its 
quality and customization in a year. With widely available 
audio cloning tools, 1 minute of audio is enough to fake a 
voice. While video remains harder to do in complex real-world 
scenarios, consumer apps can swap an individual's face onto 
another's body, strip the clothing from a woman's body. 
Matching lip movements to a new audio track in a video is being 
demoed by Google, and live deepfakes are feasible. As a result, 
we are seeing an increased volume and ease in creating 
variations of realistic synthetic photos, audio, and, 
eventually, video of specific real individuals and contexts.
    I would like to flag four future trends. These are 
increasing ease in instructing these tools in plain language; 
two, more ability to tailor outputs; three, more realistic 
outputs; and four, eventually similar advances in video to what 
we now see in audio and images.
    Moving on to the risks and harms, deepfakes are causing 
harms in the U.S. and globally, with disproportionate impacts 
on groups already at risk of discrimination or vulnerable to 
offline harms. Women and girls are widely targeted with 
nonconsensual sexual images, and this problem is escalating. 
AI-generated child sexual abuse material, CSAM, is increasing. 
Simulated audio scams are proliferating, as are misuses of AI 
audio in political contexts. My organization frequently sees 
claims of AI generation used to muddy the waters and dismiss 
critical real content, while actors and others have their 
likenesses stolen to use in non-satirical commercial contexts.
    Political processes are likely to be impacted by deepfakes, 
and recent polls find the American public is fearful of their 
impact. However, it is unreasonable to expect individuals to 
spot deceptive, yet realistic, deepfake imagery and voices. 
Guidance to look for the six-fingered hand or inspect visual 
errors in a pope in a puffer jacket does not help in the long 
run. Meanwhile, under resourced newsrooms and community leaders 
across the political spectrum are under pressure and do not 
have access to reliable tools that can detect deepfakes. That 
is because deepfake detection efforts are not yet reliable at 
scale or across multiple different ways of creating deepfakes, 
nor are there widely shared methods to clearly indicate how AI 
was used in creating content.
    This leads me to what Congress should consider, to address 
these advances in technology and accompanying misuses. First, 
enact Federal legislation around existing harms, including 
nonconsensual sexual content and the growing use of generative 
AI and CSAM. Incorporating broad consultation with groups 
working on these existing harms while safeguarding 
constitutional and human rights would help you craft 
appropriate steps. Second, since people will not be able to 
spot deepfake content with their eyes or ears, we need 
solutions to proactively add and show the provenance of AI 
content and, if desired and under certain circumstances, human-
generated content. Provenance, such as the C2PA standard 
mentioned before, means showing people how content and 
communications were made, edited, and distributed, as well as 
other information that explains the recipe. The best choices 
here will go beyond binary ``yes''/``no'' labels.
    Overall, these transparency approaches provide a signal of 
AI usage, but they do not, per se, indicate deception and must 
be accompanied by public education. Critically, these 
approaches should protect privacy and not collect, by default, 
personally identifiable information. For content that is not AI 
generated, we should be wary of how any provenance approach can 
be misused for surveillance and stifling freedom of speech.
    My third recommendation is on detection. Alongside 
indicating how the content we consume was made, there is a 
continuing need for after-the-fact detection for content 
believed to be AI generated. From witnesses' experience, the 
skills and tools to detect AI-generated media remain 
unavailable to the people who need them the most, including 
journalists, rights defenders, and election officials 
domestically and globally. It remains critical to support 
Federal research and investment in this area to improve 
detection overall and to close this gap. It should be noted 
that both provenance and detection are not as relevant to 
nonconsensual sexual deepfakes where a real versus fake is 
often beside the point since the harm is caused in other ways. 
We need other responses to that.
    As a general and final statement for both detection and 
provenance to be effective in helping the public to understand 
how deepfake technologies are used in the media we consume, we 
need a clear pipeline of responsibility that includes all the 
technology actors involved in the production of AI technologies 
more broadly, from the foundation models, to those designing 
and deploying software and apps, to the platforms that 
disseminate content. Thank you for the opportunity to testify.
    Ms. Mace. Thank you. I will now recognize Mr. Overton to 
begin your opening statement.

                      STATEMENT OF SPENCER OVERTON

                            PROFESSOR OF LAW

                    GEORGE WASHINGTON SCHOOL OF LAW

    Mr. Overton. Chairwoman Mace, Ranking Member Connolly, 
Subcommittee Members, thanks for inviting me to testify. My 
name is Spencer Overton. I am a professor at GW Law School and 
GW's Equity Institute. My research focuses on civil rights law, 
democracy, and disinformation.
    Now, while deepfake technologies offer many benefits, they 
also threaten democratic values, and they produce harms 
disproportionately borne by women and communities of color. 
Nina Jankowitz, for example, is a 34-year-old researcher. She 
specializes in state-sponsored disinformation and gendered 
online abuse. Earlier this year, she found out she was featured 
in at least three synthetic videos that appear to show her 
engaging in sex acts. Now, she wrote about these videos.
    She wrote, quote, and I will just use her words, ``Although 
they may provide cheap thrills for the viewer, their deeper 
purpose is to humiliate, shame, and objectify women, especially 
women who have the temerity to speak out. Users can also easily 
find deepfake porn videos of the singer, Taylor Swift; the 
actress Emma Watson; and the former Fox News host, Megyn Kelly. 
Democratic officials, such as Kamala Harris, Nancy Pelosi, 
Alexandria Ocasio-Cortez, and Republicans Nikki Haley and Elise 
Stefanik, and countless other prominent women. By simply 
existing as women in public life, we have all become targets, 
stripped of our accomplishments, our intellect, and our 
activism, and reduced to sex objects for the pleasure of 
millions of anonymous eyes.''
    Deepfake pornography accounts for, as you all said, over 90 
percent of all deepfake videos online. Women are featured as 
the primary subjects of 99 percent of deepfake pornography, 
while men are the primary subjects in only 1 percent. 
Nonconsensual deepfake pornography does not simply hurt women's 
feelings. It is an anti-democratic form of harassment designed 
to silence and undermine public confidence in women as 
legitimate public policy leaders. Deepfake technology is also 
fueling racial harassment. Earlier this year, a deepfake video 
showed a middle school principal saying that Black students 
should be sent back to Africa, calling them monkeys and the 
``N'' word, and threatening gun violence. User-friendly and 
affordable, deepfake technology could allow bad actors to be 
even more effective in dividing Americans and undermining 
democracy.
    So, when we think back to 2016, the Russians, we know, set 
up social media accounts pretending to be Black Americans. They 
posted calls for racial justice, developed a following, and 
then, just before Election Day, they targeted ads at Black 
users, encouraging them to boycott the election and not vote. 
Now today, in this world, the Russians or domestic bad actors 
could spark social upheaval by creating deepfake videos of a 
white police officer shooting an unarmed Black person. Indeed, 
earlier this year, just before Chicago's mayoral election, a 
deepfake video went viral of a candidate casually suggesting 
that regular police killings of civilians was normal, and that 
candidate lost. Now, the private sector is definitely 
important, but the market alone will not solve all of these 
problems. Initial studies, for example, show that deepfake 
detection systems have higher error rates for videos showing 
people of color.
    In conclusion, as deepfake technology becomes more common, 
women and communities of color bear increasing burdens. Members 
of Congress should come together, understand these emerging 
challenges, and really take action to protect all Americans and 
our democracy from the harms. Thank you.
    Ms. Mace. Thank you. I would now like to recognize myself 
for 5 minutes for questioning, and my first question is for 
every member of the panel. So, let us not do 5 minutes each 
because we only have 5 minutes today, so if you could just keep 
it brief.
    I am very concerned about deepfake technology being 
weaponized against women and children. Mr. Overton, as you made 
your point, the overwhelming majority of deepfakes circulating 
are pornographic. Most of these involve images of women. Some 
are images of children. As a woman in a public position and the 
mother of a teenage daughter, it is alarming to me how easy it 
has become to create and distribute realistic pornographic 
images of actual women and girls. These images can cause 
lifelong and lasting humiliation and harm to that woman or 
girl. So, what can we do to protect women and children from 
being abused in this manner? Mr. Ibraham, and we will just go 
across the panel.
    Mr. Ibrahim. Thank you, Congresswoman. Indeed, this is, as 
everyone noted, the main issue right now with generative AI, 
and there are no immediate silver bullets. So, several 
colleagues have noted media literacy and education and 
awareness. Also, a lot of these nonconsensual pornographic 
images are made from open-source models. There are ways in 
which open-source models can potentially leverage things, like 
provenance and watermarking----
    Ms. Mace. Mm-hmm.
    Mr. Ibrahim [continuing]. So that the outputs of those 
models will have those marks, and law enforcement can better 
detect, better trace down and take down such images. Those are 
just some thoughts to begin with.
    Ms. Mace. Thank you. Mr. Doermann?
    Mr. Doermann. One of the challenges that we have is that we 
do not have a culture where a lot of these things are 
unacceptable. In the case of New Jersey, I understand that 
there were parents even that said boys will be boys. These are 
not the kinds of things that we have today that should allow 
these types of things to progress from a technology point of 
view. We do have the ability to do partial image search. As 
another panelist here said, we have the original source 
material, and we could check that something has been 
manipulated in that way. It just requires that we do it at 
scale. It is not an easy solution, but those are the types of 
things that we have to think sort of outside the box.
    Ms. Mace. Mr. Gregory? Microphone.
    Mr. Gregory. There is a patchwork of state laws at the 
moment. There is no Federal law at the moment that would 
protect women and girls. We need to make it clearer where there 
is individual liability that can be applied here. I should also 
note that recent research indicates how easy it is to find 
these deepfakes on search engines. Just type in a name plus 
``deepfakes,'' and you will come up with a deepfake of a public 
figure. So, addressing the responsibility of platforms within 
to make sure that, it is less easy to do that because at the 
moment, it is very hard for individuals to chase down all the 
examples of their deepfakes online with individual requests.
    Ms. Mace. Mr. Overton?
    Mr. Overton. The proposed Preventing Deepfakes of Intimate 
Images Act is a good start. Having both criminal and civil 
penalties is important. Not having overly burdensome intent 
requirements to establish a violation and also focusing on both 
creators and distributors, those are some important factors.
    Ms. Mace. Thank you. My next question is about laws. Do we 
need changes in law enforcement practice? And there are revenge 
porn laws, as an example, that do not cover deepfakes 
necessarily. On the Federal level, 15 U.S. Code, Section 6851--
I happened to be reading about it today
    -is civil action related to intimate images and videos, but 
it relates to real images and videos, not to deepfakes. And so, 
I see a huge gap in law and even law enforcement practice, and 
what are your thoughts on that? Mm-hmm. We have a minute, so 
everybody gets, like, 20 seconds.
    Mr. Ibrahim. I would encourage examination of laws and what 
generative AI platforms and models can do----
    Ms. Mace. Mm-hmm.
    Mr. Ibrahim [continuing]. To pre-mark their content output 
so that we can better effectively take things down and track 
them.
    Mr. Doermann. I think I am not a legal scholar, but, you 
know, it is my understanding that, you know, the same way as 
our first generative algorithms targeted very high-level 
individuals, it might not be just pornographic. It might be 
just showing somebody in another type of compromising 
situation.
    Ms. Mace. Mm-hmm.
    Mr. Doermann. We need comprehensive laws to address these 
things.
    Ms. Mace. Mr. Gregory.
    Mr. Gregory. Extending Federal law to cover synthetic 
content----
    Ms. Mace. Mm-hmm.
    Mr. Gregory [continuing]. That fulfills the same purpose as 
a revenge porn and making sure it is accessible globally. We 
encounter cases of this all over the world.
    Ms. Mace. Thank you. Mr. Overton?
    Mr. Overton. Yes. I concur with Mr. Gregory.
    Ms. Mace. That was easy. All right. Thank you, and I will 
yield for 5 minutes to my colleague, Mr. Connolly.
    Mr. Connolly. Thank you, Madam Chairwoman. You know, it 
seems to me that this is not as simple as it seems. Let us take 
AI for pornography. So, if somebody is a caricaturist and uses 
AI, and they want to make fun of a political figure and they 
make him to be the emperor with no clothes, crown on the head, 
and he is walking around with no clothes to make the point that 
he is empty, he is without merit or politically lost, now that 
is not pornography. It is AI-generative technology. It is not 
the real thing, but the parameters of the law, being a public 
official, you have got to put up with a fair amount. On the 
other hand, if somebody took that same individual and used it 
clearly as, not a caricature, not fun, but in a pornographic 
AI-generative technology, has he crossed a line in terms of the 
law, Professor Overton?
    Mr. Overton. So, I think the answer is yes. Obviously, we 
have got to be very sensitive in terms of these First Amendment 
issues, including satire and parody, that type of thing. I 
would say, though, that even if we have disclosure in terms of 
deepfakes, this targeting of women who are political figures, 
even if it is satire, I think that it is a problem and 
something that we really need to hone in on.
    Mr. Connolly. I agree with you, but you are the professor 
of law, not me. Surely you appreciate the delicacy of that----
    Mr. Overton. Right.
    Mr. Connolly [continuing]. However, under our 
constitutional system.
    Mr. Overton. Correct.
    Mr. Connolly. We have limits for public officials to be 
able to seek redress in terms of libel.
    Mr. Overton. That is right.
    Mr. Doermann. You can defame us----
    Mr. Overton. New York Times v. Sullivan.
    Mr. Connolly. Yes. You can defame us in a way you cannot 
defame some other citizens, New York Times v. Sullivan, so we 
have high standards for public officials.
    Mr. Overton. Yes.
    Mr. Connolly. So, I put that as a category of complexity.
    Mr. Overton. Right.
    Mr. Connolly. Not so simple in terms of regulating.
    Mr. Overton. It is something we have got to grapple with. I 
really refer you to Mary Anne Franks, my colleague at GW, did a 
great Law Review article in 2019 where she really chronicled 
these harms as not really contributing to free speech and the 
marketplace of ideas and truth. And so, there is something else 
that is here, and we have got to really grapple with it.
    Mr. Connolly. Right. I agree with you. I do not think it is 
as simple----
    Mr. Overton. Right.
    Mr. Connolly [continuing]. As we would all like.
    Mr. Overton. Yep.
    Mr. Connolly. Now, that is public officials.
    Mr. Overton. Mm-hmm.
    Mr. Connolly. Private individuals, such as the girls we 
talked about in New Jersey, are pure victims of somebody else's 
perverse sense of fun or pleasure, and they suffer real harm. 
Legally, what is their protection? What is their redress right 
now?
    Mr. Overton. Yes. Right now, the problem is a lot of law 
does not cover this, and some states have laws with regard to 
deepfakes, but many states do not. Even though almost all 
states have revenge porn laws, this activity does not clearly 
fall under that, so often, there is no recourse.
    Mr. Connolly. So, we could maybe use the sort of underage 
piece of law to get at this if these victims are under a 
certain age.
    Mr. Overton. I think that that is correct, but even the 
CSAM issue that we talked about before is not always clearly 
covered here in terms of existing laws with regard to child 
pornography. So, you know, we have got some real holes in the 
law.
    Mr. Connolly. Yes. OK. Well, I think that is really worthy 
of an explanation, not only by us up here, but by your 
profession and by the academic community. Dr. Doermann, let us 
send you back in time. You are back at DARPA, and you are in 
charge of all AI research and projects. What are we not doing 
that you want to see funded? You know, pick two or three that 
we really ought to be doing right now because it could have a 
beneficial effect if we plow this ground in terms of its 
promise in protecting ourselves from deepfakes.
    Mr. Doermann. Absolutely. It is not just deepfakes, you are 
right. It is AI in general. We have gotten to the point where 
these large language models, where these models that we have 
are completely unexplainable. People believe that AI is somehow 
an answer, and everything is always right that comes out of 
these systems. We cannot converse with these systems, and they 
cannot explain why they made a decision or how they made 
things, I think the explainability issues. And these are things 
that DARPA is looking at now, but we need to have the trust and 
the safety aspects explored at the grassroots level for all of 
these things.
    Mr. Connolly. I would just say, Madam Chair, my time is up, 
but I think there is a huge difference between the pope in a 
puffery jacket, which does not do much harm, and the example of 
Ukraine, where deepfakes has him saying we are putting down our 
arms and surrendering. You know, that can cause or end a 
conflict in an undesirable way, and so clearly protecting 
ourselves and being able to counter that disinformation in a 
very expeditious way, if not preventing it to begin with, I 
think is kind of the goal. I thank you.
    Ms. Mace. Thank you, Mr. Connolly. I now recognize Mr. 
Timmons for 5 minutes.
    Mr. Timmons. Thank you, Madam Chair. It seems we have two 
main issues here. One is attribution--a lot of people would see 
a deepfake video and not know whether it was fake or real--and 
the next issue is updating our legal structures to address the 
core purpose of them. We have revenge porn laws in many states. 
The update to the Violence Against Women Act that Chairwoman 
Mace just mentioned gives a civil cause of action for $150,000. 
The purpose of that was to address this issue, and the 
legislative intent did not really keep in mind the possibility 
of a deepfake. If you cannot distinguish it, there is no 
difference.
    So, I guess my first question, Dr. Doermann, is there any 
way that we can, one, mandate identification to show that it is 
an altered image or a fake image, and then, two, is there a way 
to mandate that in the Code? Just like a photo on my iPhone 
says where it is and the GPS location it was taken, could you 
do the same thing with an IP address, and a location, and a 
time, and a date stamp on the video, and mandate that and make 
it illegal to create images using this technology without the 
attribution component? Does my question make sense?
    Mr. Doermann. Yes. I think you actually have two parts 
there. Well, first of all, you know, if you mandate creating 
content or creating things that require you to disclose, for 
example, that it is a deepfake, the adversaries and the people 
that are doing these bad things in the first place, they are 
not going to follow those rules anyway. I mean, that is a much 
lower bar than actually creating pornographic images.
    Mr. Timmons. Well, we got to take the first step of U.S. 
citizens within our jurisdiction.
    Mr. Doermann. We can, yes.
    Mr. Timmons. So, I mean, we could easily say if you create 
this, there is a civil penalty that is available, and that if 
you do this, there is a criminal penalty, just like states have 
done with revenge porn.
    Mr. Doermann. Again, there is also a continuum between the 
things that are used for good and the things that it is used 
for bad, so just saying that you are going to, you know, 
identify it.
    Mr. Timmons. OK. Deepfake porn, we could literally use AI 
to say whether something is considered porn or not and then 
whether that is----
    Mr. Doermann. I am not sure about that. I think we will 
have a continuum of these----
    Mr. Timmons. Well, you are going to have to take a photo of 
somebody initially of their face or their likeness to then give 
the AI the ability to create something that is resembling the 
original human, and you could then----
    Mr. Doermann. Well, the face is, but the original content 
can come from a legal pornographic film, for example, and that 
is what is happening.
    Mr. Timmons. But you are putting someone's face on it that 
is not the same face----
    Mr. Doermann. That is----
    Mr. Timmons [continuing]. To make it look like them in an 
attempt to do the same thing that we have revenge porn laws to 
do.
    Mr. Doermann. That is what my colleagues here are saying, 
that there are holes in these laws that do not necessarily 
allow you to do that, those----
    Mr. Timmons. Mr. Overton, is there any argument that you 
could use the existing statute to file a Federal lawsuit 
against somebody for sharing a deepfake?
    Mr. Overton. There is an argument. I think the question is, 
does it hold water.
    Mr. Timmons. We have got some judges in this country that 
do just about anything. All right.
    Mr. Overton. Right, but, you know, we want certainly some 
consistency in enforcement of law.
    Mr. Timmons. Sure. We could also maybe expand the Code 
section, too, but, I mean, I guess then it becomes the whole 
purpose of revenge porn law is, theoretically, you were 
complicit in the initial video but not complicit in the 
sharing, and it has to be done for retribution of some kind. 
So, I guess it becomes a lot more complicated when you are 
talking about celebrities and whatnot, but they also deserve 
the same degree of privacy and respect that we are seeking for 
everyone else. OK. Let us go back. I mean, I----
    Mr. Overton. Well, let me just followup here. Disclosure 
under the court is much more acceptable than complete 
restrictions here, you know, in terms of the First Amendment.
    Mr. Timmons. OK. And I guess, Dr. Doermann, back to the 
attribution issue. I mean, it is not unreasonable to try to 
create a legal framework through which a photo that is taken on 
my iPhone has all of this metadata. I mean, theoretically, if 
that metadata is not present in a video, then we would know 
that it is a malicious and does that----
    Mr. Doermann. Yes. Yes. In theory, yes. The problem comes, 
again, with enforcing this because you now are forcing 
individuals to mark their content. It is a----
    Mr. Timmons. Could we use AI to seek out and automatically 
delete videos that do not have the----
    Mr. Doermann. Absolutely, and you could forge this type of 
stuff as well. Even camera fingerprints, these things can be 
forged, so we just have to be careful about what we rely on, 
and we make sure that everybody is playing by the same rules in 
being able to enforce those types of things.
    Mr. Timmons. Theoretically, we could mandate certain 
websites to use AI to identify deepfakes and automatically 
delete them if they are deemed pornographic. Theoretically.
    Mr. Doermann. Theoretically.
    Mr. Timmons. OK. All right. Sorry, Madam Chair. Thank you. 
I yield back.
    Ms. Mace. No. Thank you. I will now recognize Mr. Burchett 
for 5 minutes.
    Mr. Burchett. Thank you, Chairlady. Several questions. I am 
just going to ramble, if it is all right with you all. This is 
a question to all of you all, and I would like to discuss how 
criminals are using this deepfake technology to produce child 
sex abuse material, just child porn. And when these criminals 
use this deepfake technology to make this material of children, 
rather than alter the images that already exist, how can law 
enforcement agencies determine the age of the subject in the 
material?
    Mr. Ibrahim. Yes, sir. We have seen a rising amount of 
cases. The New Jersey one was noted. There was also a recent 
case in Spain in which these models were used for underage 
young girls. In terms of how can law enforcement potentially 
use that information and detect, there is some growing thinking 
that if the models themselves add some watermark or provenance 
to everything----
    Mr. Burchett. And explain to me the watermark. What is 
that?
    Mr. Ibrahim. So, it----
    Mr. Burchett. I know what it means, but maybe explain it.
    Mr. Ibrahim. It would be an invisible algorithm that is 
attached to every image that is spit out of the generator, 
whether it is a benign or malicious image, and which could be 
decoded by a law enforcement agency or has some sort of chain 
of custody, that could potentially be useful. That is something 
that a variety of organizations are looking at.
    Mr. Burchett. All right.
    Mr. Doermann. We also have to educate our legal system on 
how to use these things. I was on a panel at AAAS about a month 
ago, and very simple things such as the use of face recognition 
as an AI tool, which we know has been controversial, to say the 
least. So, we just have to make sure that our content providers 
or service providers are on board and that they are sharing 
this type of information with each other, and that is 
definitely something that is doable.
    Mr. Gregory. The problem this is creating in CSAM has 
similarities to other problems. It is a volume problem where 
you then have a triage problem for the people who have to do 
the investigation because of creating synthesized images and 
adapting existing images. So, investing in tools that are 
available to law enforcement as well as others who have to do 
that triage work would be a critical next step.
    Mr. Overton. I just would note these evidentiary issues are 
very challenging for law enforcement.
    Mr. Burchett. Yes. I think the court cases are that if they 
just generate, like, a fake face, that they cannot be held for 
child porn. Is that correct?
    Mr. Doermann. It is my understanding that those laws are 
changing, but the previous laws--again, I am not a legal 
scholar--required a victim, and that was really the loophole 
there.
    Mr. Burchett. Yes.
    Mr. Doermann. But they are closing those.
    Mr. Burchett. OK. Well, how can we prevent this technology 
from being used to create child sex abuse material? Mr. 
Overton, we will start with you first, brother.
    Mr. Overton. I think that legislation is very important 
that directly deals with the CSAM issue.
    Mr. Gregory. I will agree with Mr. Overton. It is 
legislation around CSAM. There are clear reasons to legislate 
and include this alongside non-synthetic images.
    Mr. Doermann. And I will just emphasize from the technology 
point of view, this is a genie that is out of the bottle. We 
are not going to put it back in.
    Mr. Burchett. Yes.
    Mr. Doermann. These types of technologies are out there. 
They could use it on adults, consenting adults, the same way, 
and it is going to be difficult to legislate those, but the 
technology is there. It is on the laptops of anyone that wants 
to download it from the internet, and so the legislation part 
is the best approach.
    Mr. Ibrahim. I echo my colleagues, and I would just note 
the majority of these materials are often done through open-
source models. So, the more models you can get to sign up to 
frameworks and guardrails, pushing bad actors into other models 
that are harder to use would be beneficial.
    Mr. Burchett. Do you all agree that these AI-generated 
images of children, real or not, should be illegal?
    Mr. Ibrahim. I do.
    Mr. Doermann. Absolutely.
    Mr. Gregory. Yes.
    Mr. Overton. Yes. We assume they are not hurting children. 
They are, yes.
    Mr. Burchett. Yes. You know, I sponsored some legislation 
in Tennessee where actually people that abused children were 
given the death penalty, and my statement was that they had 
given these kids a lifetime sentence, and there is no coming 
back from that. It is a lifetime of guilt, and you see a lot of 
the kids self-inflict wounds and take their own lives, and it 
is just brutal. So anyway, thank you all. Chairlady?
    Ms. Mace. Yes, thank you. I will now recognize Mr. Burlison 
for 5 minutes.
    Mr. Burlison. Thank you. I will begin with Mr. Doermann. 
Can we talk about the technology that is possibly available to 
recognize or identify AI and how that might be applied in a 
commercial setting?
    Mr. Doermann. Well, the biggest challenge of any of these 
technologies is the scale. Even if you have an algorithm that 
works at 99.9 percent, the amount of data that we have at scale 
that run through our major service providers makes the false 
alarm rates almost prohibitive. We need to have a business 
model that our service providers, our content providers have 
that makes them want to take these things down. If they get 
clicks, that is what is selling ads now, and if it is not 
illegal, if we cannot hold them responsible in any way, it 
makes it very difficult to convince them to do anything.
    Mr. Burlison. Given that currently, today, there is the 
ability to identify graphic content, violent content, and then 
to often block it or require a person to take some action to 
basically break the glass and go through and see that content, 
can that not be applied to deepfake images or things that are 
created with it? That way, the individual would at least know 
that there is no truth to this image.
    Mr. Doermann. Absolutely, I mean, but the places that this 
content is showing up are on those pay sites or onsites where 
it is mixed in with other type of content, so just detecting it 
is not necessarily the issue.
    Mr. Burlison. And they do not have a self-interest, a 
financial interest in identifying the deepfakes.
    Mr. Doermann. Correct.
    Mr. Burlison. OK. I understand what you are saying. So let 
me ask, the technology is capable. We just need to identify----
    Mr. Doermann. Not necessarily the deepfake part. If you do 
a reverse image search, for example, in a number of different 
sites, you can do it for the entire image, but if you take just 
the face of an individual, that is what we really need. We need 
to be able to say, OK, we are not going to look at the entire 
video because this video is not real, right? Well, part of it 
is real. The video part of the nude body is real, and the face 
is generated. So, if we could identify those people, make sure 
that we have that consent before it gets spread, that might be 
one thing, but, you know, just to detect something as being 
pornographic, we are still not detecting the fact that it was 
generated with a fake.
    Mr. Burlison. The deepfake. You are saying that there is 
technology, AI, that can view videos and images and ascertain 
whether it is a deepfake?
    Mr. Doermann. Not reliably enough.
    Mr. Burlison. There currently is not any.
    Mr. Doermann. This is 85 percent maybe, and every time we 
release a tool that detects this, our adversaries can use AI to 
cover up that trace evidence. So, no, that is why I said in my 
opening statement that detection and trying to pull this stuff 
down is no longer a solution. We need to have a much bigger 
solution.
    Mr. Burlison. Mr. Gregory, how do we authenticate the 
content without creating a surveillance state or suppressing 
free expression?
    Mr. Gregory. The first thing I would say is, as we are 
looking at authenticity and provenance measures, ways that you 
can show how something was made with AI and perhaps with human 
inputs, and I think we should recognize that the future of 
media production is complex. It will not just be a yes or no of 
AI. It will be, yes, AI was used here, no, AI was not, here is 
a human. So, it is really important that we actually focus this 
on the how media was made, not the who, right? So, you should 
know, for example, that a piece of media was made with an AI 
tool, was altered perhaps to change an element of it, and that 
might be the critical information rather than a political 
satirist made this piece of media, which, you know, certainly 
would not be something we would want to see here in the U.S. 
and globally when you look at how that could be misused.
    So, I think we are entering a complicated scenario where it 
is both the authenticity tools and also detection tools, my 
experience, of a very messy reality where we need to focus on 
both, but we have got to do it with civil liberties and privacy 
at heart.
    Mr. Burlison. OK. As I understand it, there is--and this is 
a question for Mr. Ibrahim. What is the difference between 
detecting deepfakes and content authentication?
    Mr. Ibrahim. Detecting deepfakes would be something you do 
after the fact. It would be a system that would look at a video 
and try to spit out a binary or results-based response. Content 
authentication or provenance does it while it is being created. 
That is what Truepic does. So, as the image is being captured 
from a phone or a smartphone or a piece of hardware, you are 
attaching and cryptographically hashing the time, the date, the 
location, et cetera, into the media file while it is being 
created, so it is a proactive measure versus a reactive 
measure.
    Mr. Burlison. My time has expired.
    Ms. Mace. Thank you. I want to thank all of our witnesses 
for being here today. In closing, I want to thank you for your 
testimony again.
    So, with that, and without objection, all Members will have 
5 legislative of days within which to submit materials and to 
submit additional written questions for the witnesses, which 
will be forwarded to the witnesses for their response.
    Ms. Mace. So, if there is no further business, without 
objection, the Subcommittee stands adjourned.
    [Whereupon, at 4:43 p.m., the Subcommittee was adjourned.]

                                 [all]