An AI-generated nude photo scandal has shut down a Pennsylvania private school. On Monday, classes were canceled after parents forced leaders to either resign or face a lawsuit potentially seeking criminal penalties and accusing the school of skipping mandatory reporting of the harmful images.
The outcry erupted after a single student created sexually explicit AI images of nearly 50 female classmates at Lancaster Country Day School, Lancaster Online reported.
Head of School Matt Micciche seemingly first learned of the problem in November 2023, when a student anonymously reported the explicit deepfakes through a school portal run by the state attorney’s general office called “Safe2Say Something.” But Micciche allegedly did nothing, allowing more students to be targeted for months until police were tipped off in mid-2024.
Cops arrested the student accused of creating the harmful content in August. The student’s phone was seized as cops investigated the origins of the AI-generated images. But that arrest was not enough justice for parents who were shocked by the school’s failure to uphold mandatory reporting responsibilities following any suspicion of child abuse. They filed a court summons threatening to sue last week unless the school leaders responsible for the mishandled response resigned within 48 hours.
This tactic successfully pushed Micciche and the school board’s president, Angela Ang-Alhadeff, to “part ways” with the school, both resigning effective late Friday, Lancaster Online reported.
In a statement announcing that classes were canceled Monday, Lancaster Country Day School—which, according to Wikipedia, serves about 600 students in pre-kindergarten through high school—offered support during this “difficult time” for the community.
Parents do not seem ready to drop the suit, as the school leaders seemingly dragged their feet and resigned two days after their deadline. The parents’ lawyer, Matthew Faranda-Diedrich, told Lancaster Online Monday that “the lawsuit would still be pursued despite executive changes.”
Enlarge/ Under C2PA, this stock image would be labeled as a real photograph if the camera used to take it, and the toolchain for retouching it, supported the C2PA. But even as a real photo, does it actually represent reality, and is there a technological solution to that problem?
On Tuesday, Google announced plans to implement content authentication technology across its products to help users distinguish between human-created and AI-generated images. Over several upcoming months, the tech giant will integrate the Coalition for Content Provenance and Authenticity (C2PA) standard, a system designed to track the origin and editing history of digital content, into its search, ads, and potentially YouTube services. However, it’s an open question of whether a technological solution can address the ancient social issue of trust in recorded media produced by strangers.
A group of tech companies created the C2PA system beginning in 2019 in an attempt to combat misleading, realistic synthetic media online. As AI-generated content becomes more prevalent and realistic, experts have worried that it may be difficult for users to determine the authenticity of images they encounter. The C2PA standard creates a digital trail for content, backed by an online signing authority, that includes metadata information about where images originate and how they’ve been modified.
Google will incorporate this C2PA standard into its search results, allowing users to see if an image was created or edited using AI tools. The tech giant’s “About this image” feature in Google Search, Lens, and Circle to Search will display this information when available.
In a blog post, Laurie Richardson, Google’s vice president of trust and safety, acknowledged the complexities of establishing content provenance across platforms. She stated, “Establishing and signaling content provenance remains a complex challenge, with a range of considerations based on the product or service. And while we know there’s no silver bullet solution for all content online, working with others in the industry is critical to create sustainable and interoperable solutions.”
The company plans to use the C2PA’s latest technical standard, version 2.1, which reportedly offers improved security against tampering attacks. Its use will extend beyond search since Google intends to incorporate C2PA metadata into its ad systems as a way to “enforce key policies.” YouTube may also see integration of C2PA information for camera-captured content in the future.
Google says the new initiative aligns with its other efforts toward AI transparency, including the development of SynthID, an embedded watermarking technology created by Google DeepMind.
Widespread C2PA efficacy remains a dream
Despite having a history that reaches back at least five years now, the road to useful content provenance technology like C2PA is steep. The technology is entirely voluntary, and key authenticating metadata can easily be stripped from images once added.
AI image generators would need to support the standard for C2PA information to be included in each generated file, which will likely preclude open source image synthesis models like Flux. So perhaps, in practice, more “authentic,” camera-authored media will be labeled with C2PA than AI-generated images.
Beyond that, maintaining the metadata requires a complete toolchain that supports C2PA every step along the way, including at the source and any software used to edit or retouch the images. Currently, only a handful of camera manufacturers, such as Leica, support the C2PA standard. Nikon and Canon have pledged to adopt it, but The Verge reports that there’s still uncertainty about whether Apple and Google will implement C2PA support in their smartphone devices.
Adobe’s Photoshop and Lightroom can add and maintain C2PA data, but many other popular editing tools do not yet offer the capability. It only takes one non-compliant image editor in the chain to break the full usefulness of C2PA. And the general lack of standardized viewing methods for C2PA data across online platforms presents another obstacle to making the standard useful for everyday users.
Currently, C2PA could arguably be seen as a technological solution for current trust issues around fake images. In that sense, C2PA may become one of many tools used to authenticate content by determining whether the information came from a credible source—if the C2PA metadata is preserved—but it is unlikely to be a complete solution to AI-generated misinformation on its own.
Cops are now using AI to generate images of fake kids, which are helping them catch child predators online, a lawsuit filed by the state of New Mexico against Snapchat revealed this week.
According to the complaint, the New Mexico Department of Justice launched an undercover investigation in recent months to prove that Snapchat “is a primary social media platform for sharing child sexual abuse material (CSAM)” and sextortion of minors, because its “algorithm serves up children to adult predators.”
As part of their probe, an investigator “set up a decoy account for a 14-year-old girl, Sexy14Heather.”
An AI-generated image of “Sexy14Heather” included in the New Mexico complaint.
An image of a Snapchat avatar for “Sexy14Heather” included in the New Mexico complaint.
Despite Snapchat setting the fake minor’s profile to private and the account not adding any followers, “Heather” was soon recommended widely to “dangerous accounts, including ones named ‘child.rape’ and ‘pedo_lover10,’ in addition to others that are even more explicit,” the New Mexico DOJ said in a press release.
And after “Heather” accepted a follow request from just one account, the recommendations got even worse. “Snapchat suggested over 91 users, including numerous adult users whose accounts included or sought to exchange sexually explicit content,” New Mexico’s complaint alleged.
“Snapchat is a breeding ground for predators to collect sexually explicit images of children and to find, groom, and extort them,” New Mexico’s complaint alleged.
Posing as “Sexy14Heather,” the investigator swapped messages with adult accounts, including users who “sent inappropriate messages and explicit photos.” In one exchange with a user named “50+ SNGL DAD 4 YNGR,” the fake teen “noted her age, sent a photo, and complained about her parents making her go to school,” prompting the user to send “his own photo” as well as sexually suggestive chats. Other accounts asked “Heather” to “trade presumably explicit content,” and several “attempted to coerce the underage persona into sharing CSAM,” the New Mexico DOJ said.
“Heather” also tested out Snapchat’s search tool, finding that “even though she used no sexually explicit language, the algorithm must have determined that she was looking for CSAM” when she searched for other teen users. It “began recommending users associated with trading” CSAM, including accounts with usernames such as “naughtypics,” “addfortrading,” “teentr3de,” “gayhorny13yox,” and “teentradevirgin,” the investigation found, “suggesting that these accounts also were involved in the dissemination of CSAM.”
This novel use of AI was prompted after Albuquerque police indicted a man, Alejandro Marquez, who pled guilty and was sentenced to 18 years for raping an 11-year-old girl he met through Snapchat’s Quick Add feature in 2022, New Mexico’s complaint said. More recently, the New Mexico complaint said, an Albuquerque man, Jeremy Guthrie, was arrested and sentenced this summer for “raping a 12-year-old girl who he met and cultivated over Snapchat.”
In the past, police have posed as kids online to catch child predators using photos of younger-looking adult women or even younger photos of police officers. Using AI-generated images could be considered a more ethical way to conduct these stings, a lawyer specializing in sex crimes, Carrie Goldberg, told Ars, because “an AI decoy profile is less problematic than using images of an actual child.”
But using AI could complicate investigations and carry its own ethical concerns, Goldberg warned, as child safety experts and law enforcement warn that the Internet is increasingly swamped with AI-generated CSAM.
“In terms of AI being used for entrapment, defendants can defend themselves if they say the government induced them to commit a crime that they were not already predisposed to commit,” Goldberg told Ars. “Of course, it would be ethically concerning if the government were to create deepfake AI child sexual abuse material (CSAM), because those images are illegal, and we don’t want more CSAM in circulation.”
Experts have warned that AI image generators should never be trained on datasets that combine images of real kids with explicit content to avoid any instances of AI-generated CSAM, which is particularly harmful when it appears to depict a real kid or an actual victim of child abuse.
In the New Mexico complaint, only one AI-generated image is included, so it’s unclear how widely the state’s DOJ is using AI or if cops are possibly using more advanced methods to generate multiple images of the same fake kid. It’s also unclear what ethical concerns were weighed before cops began using AI decoys.
The New Mexico DOJ did not respond to Ars’ request for comment.
Goldberg told Ars that “there ought to be standards within law enforcement with how to use AI responsibly,” warning that “we are likely to see more entrapment defenses centered around AI if the government is using the technology in a manipulative way to pressure somebody into committing a crime.”
Artists defending a class-action lawsuit are claiming a major win this week in their fight to stop the most sophisticated AI image generators from copying billions of artworks to train AI models and replicate their styles without compensating artists.
In an order on Monday, US district judge William Orrick denied key parts of motions to dismiss from Stability AI, Midjourney, Runway AI, and DeviantArt. The court will now allow artists to proceed with discovery on claims that AI image generators relying on Stable Diffusion violate both the Copyright Act and the Lanham Act, which protects artists from commercial misuse of their names and unique styles.
“We won BIG,” an artist plaintiff, Karla Ortiz, wrote on X (formerly Twitter), celebrating the order. “Not only do we proceed on our copyright claims,” but “this order also means companies who utilize” Stable Diffusion models and LAION-like datasets that scrape artists’ works for AI training without permission “could now be liable for copyright infringement violations, amongst other violations.”
Lawyers for the artists, Joseph Saveri and Matthew Butterick, told Ars that artists suing “consider the Court’s order a significant step forward for the case,” as “the Court allowed Plaintiffs’ core copyright-infringement claims against all four defendants to proceed.”
Stability AI was the only company that responded to Ars’ request to comment, but it declined to comment.
Artists prepare to defend their livelihoods from AI
To get to this stage of the suit, artists had to amend their complaint to better explain exactly how AI image generators work to allegedly train on artists’ images and copy artists’ styles.
For example, they were told that if they “contend Stable Diffusion contains ‘compressed copies’ of the Training Images, they need to define ‘compressed copies’ and explain plausible facts in support. And if plaintiffs’ compressed copies theory is based on a contention that Stable Diffusion contains mathematical or statistical methods that can be carried out through algorithms or instructions in order to reconstruct the Training Images in whole or in part to create the new Output Images, they need to clarify that and provide plausible facts in support,” Orrick wrote.
To keep their fight alive, the artists pored through academic articles to support their arguments that “Stable Diffusion is built to a significant extent on copyrighted works and that the way the product operates necessarily invokes copies or protected elements of those works.” Orrick agreed that their amended complaint made plausible inferences that “at this juncture” is enough to support claims “that Stable Diffusion by operation by end users creates copyright infringement and was created to facilitate that infringement by design.”
“Specifically, the Court found Plaintiffs’ theory that image-diffusion models like Stable Diffusion contain compressed copies of their datasets to be plausible,” Saveri and Butterick’s statement to Ars said. “The Court also found it plausible that training, distributing, and copying such models constitute acts of copyright infringement.”
Not all of the artists’ claims survived, with Orrick granting motions to dismiss claims alleging that AI companies removed content management information from artworks in violation of the Digital Millennium Copyright Act (DMCA). Because artists failed to show evidence of defendants altering or stripping this information, they must permanently drop the DMCA claims.
Part of Orrick’s decision on the DMCA claims, however, indicates that the legal basis for dismissal is “unsettled,” with Orrick simply agreeing with Stability AI’s unsettled argument that “because the output images are admittedly not identical to the Training Images, there can be no liability for any removal of CMI that occurred during the training process.”
Ortiz wrote on X that she respectfully disagreed with that part of the decision but expressed enthusiasm that the court allowed artists to proceed with false endorsement claims, alleging that Midjourney violated the Lanham Act.
Five artists successfully argued that because “their names appeared on the list of 4,700 artists posted by Midjourney’s CEO on Discord” and that list was used to promote “the various styles of artistic works its AI product could produce,” this plausibly created confusion over whether those artists had endorsed Midjourney.
“Whether or not a reasonably prudent consumer would be confused or misled by the Names List and showcase to conclude that the included artists were endorsing the Midjourney product can be tested at summary judgment,” Orrick wrote. “Discovery may show that it is or that is it not.”
While Orrick agreed with Midjourney that “plaintiffs have no protection over ‘simple, cartoony drawings’ or ‘gritty fantasy paintings,'” artists were able to advance a “trade dress” claim under the Lanham Act, too. This is because Midjourney allegedly “allows users to create works capturing the ‘trade dress of each of the Midjourney Named Plaintiffs [that] is inherently distinctive in look and feel as used in connection with their artwork and art products.'”
As discovery proceeds in the case, artists will also have an opportunity to amend dismissed claims of unjust enrichment. According to Orrick, their next amended complaint will be their last chance to prove that AI companies have “deprived plaintiffs ‘the benefit of the value of their works.'”
Saveri and Butterick confirmed that “though the Court dismissed certain supplementary claims, Plaintiffs’ central claims will now proceed to discovery and trial.” On X, Ortiz suggested that the artists’ case is “now potentially one of THE biggest copyright infringement and trade dress cases ever!”
“Looking forward to the next stage of our fight!” Ortiz wrote.
A Spanish youth court has sentenced 15 minors to one year of probation after spreading AI-generated nude images of female classmates in two WhatsApp groups.
The minors were charged with 20 counts of creating child sex abuse images and 20 counts of offenses against their victims’ moral integrity. In addition to probation, the teens will also be required to attend classes on gender and equality, as well as on the “responsible use of information and communication technologies,” a press release from the Juvenile Court of Badajoz said.
Many of the victims were too ashamed to speak up when the inappropriate fake images began spreading last year. Prior to the sentencing, a mother of one of the victims told The Guardian that girls like her daughter “were completely terrified and had tremendous anxiety attacks because they were suffering this in silence.”
The court confirmed that the teens used artificial intelligence to create images where female classmates “appear naked” by swiping photos from their social media profiles and superimposing their faces on “other naked female bodies.”
Teens using AI to sexualize and harass classmates has become an alarming global trend. Police have probed disturbing cases in both high schools and middle schools in the US, and earlier this year, the European Union proposed expanding its definition of child sex abuse to more effectively “prosecute the production and dissemination of deepfakes and AI-generated material.” Last year, US President Joe Biden issued an executive order urging lawmakers to pass more protections.
In addition to mental health impacts, victims have reported losing trust in classmates who targeted them and wanting to switch schools to avoid further contact with harassers. Others stopped posting photos online and remained fearful that the harmful AI images will resurface.
Minors targeting classmates may not realize exactly how far images can potentially spread when generating fake child sex abuse materials (CSAM); they could even end up on the dark web. An investigation by the United Kingdom-based Internet Watch Foundation (IWF) last year reported that “20,254 AI-generated images were found to have been posted to one dark web CSAM forum in a one-month period,” with more than half determined most likely to be criminal.
IWF warned that it has identified a growing market for AI-generated CSAM and concluded that “most AI CSAM found is now realistic enough to be treated as ‘real’ CSAM.” One “shocked” mother of a female classmate victimized in Spain agreed. She told The Guardian that “if I didn’t know my daughter’s body, I would have thought that image was real.”
More drastic steps to stop deepfakes
While lawmakers struggle to apply existing protections against CSAM to AI-generated images or to update laws to explicitly prosecute the offense, other more drastic solutions to prevent the harmful spread of deepfakes have been proposed.
In an op-ed for The Guardian today, journalist Lucia Osborne-Crowley advocated for laws restricting sites used to both generate and surface deepfake pornography, including regulating this harmful content when it appears on social media sites and search engines. And IWF suggested that, like jurisdictions that restrict sharing bomb-making information, lawmakers could also restrict guides instructing bad actors on how to use AI to generate CSAM.
The Malvaluna Association, which represented families of victims in Spain and broadly advocates for better sex education, told El Diario that beyond more regulations, more education is needed to stop teens motivated to use AI to attack classmates. Because the teens were ordered to attend classes, the association agreed to the sentencing measures.
“Beyond this particular trial, these facts should make us reflect on the need to educate people about equality between men and women,” the Malvaluna Association said. The group urged that today’s kids should not be learning about sex through pornography that “generates more sexism and violence.”
Teens sentenced in Spain were between the ages of 13 and 15. According to the Guardian, Spanish law prevented sentencing of minors under 14, but the youth court “can force them to take part in rehabilitation courses.”
Tech companies could also make it easier to report and remove harmful deepfakes. Ars could not immediately reach Meta for comment on efforts to combat the proliferation of AI-generated CSAM on WhatsApp, the private messaging app that was used to share fake images in Spain.
An FAQ said that “WhatsApp has zero tolerance for child sexual exploitation and abuse, and we ban users when we become aware they are sharing content that exploits or endangers children,” but it does not mention AI.
As the US moves toward criminalizing deepfakes—deceptive AI-generated audio, images, and videos that are increasingly hard to discern from authentic content online—tech companies have rushed to roll out tools to help everyone better detect AI content.
But efforts so far have been imperfect, and experts fear that social media platforms may not be ready to handle the ensuing AI chaos during major global elections in 2024—despite tech giants committing to making tools specifically to combat AI-fueled election disinformation. The best AI detection remains observant humans, who, by paying close attention to deepfakes, can pick up on flaws like AI-generated people with extra fingers or AI voices that speak without pausing for a breath.
Among the splashiest tools announced this week, OpenAI shared details today about a new AI image detection classifier that it claims can detect about 98 percent of AI outputs from its own sophisticated image generator, DALL-E 3. It also “currently flags approximately 5 to 10 percent of images generated by other AI models,” OpenAI’s blog said.
According to OpenAI, the classifier provides a binary “true/false” response “indicating the likelihood of the image being AI-generated by DALL·E 3.” A screenshot of the tool shows how it can also be used to display a straightforward content summary confirming that “this content was generated with an AI tool” and includes fields ideally flagging the “app or device” and AI tool used.
To develop the tool, OpenAI spent months adding tamper-resistant metadata to “all images created and edited by DALL·E 3” that “can be used to prove the content comes” from “a particular source.” The detector reads this metadata to accurately flag DALL-E 3 images as fake.
That metadata follows “a widely used standard for digital content certification” set by the Coalition for Content Provenance and Authenticity (C2PA), often likened to a nutrition label. And reinforcing that standard has become “an important aspect” of OpenAI’s approach to AI detection beyond DALL-E 3, OpenAI said. When OpenAI broadly launches its video generator, Sora, C2PA metadata will be integrated into that tool as well, OpenAI said.
Of course, this solution is not comprehensive because that metadata could always be removed, and “people can still create deceptive content without this information (or can remove it),” OpenAI said, “but they cannot easily fake or alter this information, making it an important resource to build trust.”
Because OpenAI is all in on C2PA, the AI leader announced today that it would join the C2PA steering committee to help drive broader adoption of the standard. OpenAI will also launch a $2 million fund with Microsoft to support broader “AI education and understanding,” seemingly partly in the hopes that the more people understand about the importance of AI detection, the less likely they will be to remove this metadata.
“As adoption of the standard increases, this information can accompany content through its lifecycle of sharing, modification, and reuse,” OpenAI said. “Over time, we believe this kind of metadata will be something people come to expect, filling a crucial gap in digital content authenticity practices.”
OpenAI joining the committee “marks a significant milestone for the C2PA and will help advance the coalition’s mission to increase transparency around digital media as AI-generated content becomes more prevalent,” C2PA said in a blog.
Enlarge/ Stable Diffusion 3 generation with the prompt: studio photograph closeup of a chameleon over a black background.
On Thursday, Stability AI announced Stable Diffusion 3, an open-weights next-generation image-synthesis model. It follows its predecessors by reportedly generating detailed, multi-subject images with improved quality and accuracy in text generation. The brief announcement was not accompanied by a public demo, but Stability is opening up a waitlist today for those who would like to try it.
Stability says that its Stable Diffusion 3 family of models (which takes text descriptions called “prompts” and turns them into matching images) range in size from 800 million to 8 billion parameters. The size range accommodates allowing different versions of the model to run locally on a variety of devices—from smartphones to servers. Parameter size roughly corresponds to model capability in terms of how much detail it can generate. Larger models also require more VRAM on GPU accelerators to run.
Since 2022, we’ve seen Stability launch a progression of AI image-generation models: Stable Diffusion 1.4, 1.5, 2.0, 2.1, XL, XL Turbo, and now 3. Stability has made a name for itself as providing a more open alternative to proprietary image-synthesis models like OpenAI’s DALL-E 3, though not without controversy due to the use of copyrighted training data, bias, and the potential for abuse. (This has led to lawsuits that are unresolved.) Stable Diffusion models have been open-weights and source-available, which means the models can be run locally and fine-tuned to change their outputs.
Stable Diffusion 3 generation with the prompt: Epic anime artwork of a wizard atop a mountain at night casting a cosmic spell into the dark sky that says “Stable Diffusion 3” made out of colorful energy.
An AI-generated image of a grandma wearing a “Go big or go home sweatshirt” generated by Stable Diffusion 3.
Stable Diffusion 3 generation with the prompt: Three transparent glass bottles on a wooden table. The one on the left has red liquid and the number 1. The one in the middle has blue liquid and the number 2. The one on the right has green liquid and the number 3.
An AI-generated image created by Stable Diffusion 3.
Stable Diffusion 3 generation with the prompt: A horse balancing on top of a colorful ball in a field with green grass and a mountain in the background.
Stable Diffusion 3 generation with the prompt: Moody still life of assorted pumpkins.
Stable Diffusion 3 generation with the prompt: a painting of an astronaut riding a pig wearing a tutu holding a pink umbrella, on the ground next to the pig is a robin bird wearing a top hat, in the corner are the words “stable diffusion.”
Stable Diffusion 3 generation with the prompt: Resting on the kitchen table is an embroidered cloth with the text ‘good night’ and an embroidered baby tiger. Next to the cloth there is a lit candle. The lighting is dim and dramatic.
Stable Diffusion 3 generation with the prompt: Photo of an 90’s desktop computer on a work desk, on the computer screen it says “welcome”. On the wall in the background we see beautiful graffiti with the text “SD3” very large on the wall.
As far as tech improvements are concerned, Stability CEO Emad Mostaque wrote on X, “This uses a new type of diffusion transformer (similar to Sora) combined with flow matching and other improvements. This takes advantage of transformer improvements & can not only scale further but accept multimodal inputs.”
Like Mostaque said, the Stable Diffusion 3 family uses diffusion transformer architecture, which is a new way of creating images with AI that swaps out the usual image-building blocks (such as U-Net architecture) for a system that works on small pieces of the picture. The method was inspired by transformers, which are good at handling patterns and sequences. This approach not only scales up efficiently but also reportedly produces higher-quality images.
Stable Diffusion 3 also utilizes “flow matching,” which is a technique for creating AI models that can generate images by learning how to transition from random noise to a structured image smoothly. It does this without needing to simulate every step of the process, instead focusing on the overall direction or flow that the image creation should follow.
Enlarge/ A comparison of outputs between OpenAI’s DALL-E 3 and Stable Diffusion 3 with the prompt, “Night photo of a sports car with the text “SD3″ on the side, the car is on a race track at high speed, a huge road sign with the text ‘faster.'”
We do not have access to Stable Diffusion 3 (SD3), but from samples we found posted on Stability’s website and associated social media accounts, the generations appear roughly comparable to other state-of-the-art image-synthesis models at the moment, including the aforementioned DALL-E 3, Adobe Firefly, Imagine with Meta AI, Midjourney, and Google Imagen.
SD3 appears to handle text generation very well in the examples provided by others, which are potentially cherry-picked. Text generation was a particular weakness of earlier image-synthesis models, so an improvement to that capability in a free model is a big deal. Also, prompt fidelity (how closely it follows descriptions in prompts) seems to be similar to DALL-E 3, but we haven’t tested that ourselves yet.
While Stable Diffusion 3 isn’t widely available, Stability says that once testing is complete, its weights will be free to download and run locally. “This preview phase, as with previous models,” Stability writes, “is crucial for gathering insights to improve its performance and safety ahead of an open release.”
Stability has been experimenting with a variety of image-synthesis architectures recently. Aside from SDXL and SDXL Turbo, just last week, the company announced Stable Cascade, which uses a three-stage process for text-to-image synthesis.
Law enforcement is continuing to warn that a “flood” of AI-generated fake child sex images is making it harder to investigate real crimes against abused children, The New York Times reported.
Last year, after researchers uncovered thousands of realistic but fake AI child sex images online, quickly every attorney general across the US called on Congress to set up a committee to squash the problem. But so far, Congress has moved slowly, while only a few states have specifically banned AI-generated non-consensual intimate imagery. Meanwhile, law enforcement continues to struggle with figuring out how to confront bad actors found to be creating and sharing images that, for now, largely exist in a legal gray zone.
“Creating sexually explicit images of children through the use of artificial intelligence is a particularly heinous form of online exploitation,” Steve Grocki, the chief of the Justice Department’s child exploitation and obscenity section, told The Times. Experts told The Washington Post in 2023 that risks of realistic but fake images spreading included normalizing child sexual exploitation, luring more children into harm’s way, and making it harder for law enforcement to find actual children being harmed.
In one example, the FBI announced earlier this year that an American Airlines flight attendant, Estes Carter Thompson III, was arrested “for allegedly surreptitiously recording or attempting to record a minor female passenger using a lavatory aboard an aircraft.” A search of Thompson’s iCloud revealed “four additional instances” where Thompson allegedly recorded other minors in the lavatory, as well as “over 50 images of a 9-year-old unaccompanied minor” sleeping in her seat. While police attempted to identify these victims, they also “further alleged that hundreds of images of AI-generated child pornography” were found on Thompson’s phone.
The troubling case seems to illustrate how AI-generated child sex images can be linked to real criminal activity while also showing how police investigations could be bogged down by attempts to distinguish photos of real victims from AI images that could depict real or fake children.
Robin Richards, the commander of the Los Angeles Police Department’s Internet Crimes Against Children task force, confirmed to the NYT that due to AI, “investigations are way more challenging.”
And because image generators and AI models that can be trained on photos of children are widely available, “using AI to alter photos” of children online “is becoming more common,” Michael Bourke—a former chief psychologist for the US Marshals Service who spent decades supporting investigations into sex offenses involving children—told the NYT. Richards said that cops don’t know what to do when they find these AI-generated materials.
Currently, there aren’t many cases involving AI-generated child sex abuse materials (CSAM), The NYT reported, but experts expect that number will “grow exponentially,” raising “novel and complex questions of whether existing federal and state laws are adequate to prosecute these crimes.”
Platforms struggle to monitor harmful AI images
At a Senate Judiciary Committee hearing today grilling Big Tech CEOs over child sexual exploitation (CSE) on their platforms, Linda Yaccarino—CEO of X (formerly Twitter)—warned in her opening statement that artificial intelligence is also making it harder for platforms to monitor CSE. Yaccarino suggested that industry collaboration is imperative to get ahead of the growing problem, as is providing more resources to law enforcement.
However, US law enforcement officials have indicated that platforms are also making it harder to police CSAM and CSE online. Platforms relying on AI to detect CSAM are generating “unviable reports” gumming up investigations managed by already underfunded law enforcement teams, The Guardian reported. And the NYT reported that other investigations are being thwarted by adding end-to-end encryption options to messaging services, which “drastically limit the number of crimes the authorities are able to track.”
The NYT report noted that in 2002, the Supreme Court struck down a law that had been on the books since 1996 preventing “virtual” or “computer-generated child pornography.” South Carolina’s attorney general, Alan Wilson, has said that AI technology available today may test that ruling, especially if minors continue to be harmed by fake AI child sex images spreading online. In the meantime, federal laws such as obscenity statutes may be used to prosecute cases, the NYT reported.
Congress has recently re-introduced some legislation to directly address AI-generated non-consensual intimate images after a wide range of images depicting fake AI porn of pop star Taylor Swift went viral this month. That includes the Disrupt Explicit Forged Images and Non-Consensual Edits Act, which creates a federal civil remedy for any victims of any age who are identifiable in AI images depicting them as nude or engaged in sexually explicit conduct or sexual scenarios.
There’s also the “Preventing Deepfakes of Intimate Images Act,” which seeks to “prohibit the non-consensual disclosure of digitally altered intimate images.” That was re-introduced this year after teen boys generated AI fake nude images of female classmates and spread them around a New Jersey high school last fall. Francesca Mani, one of the teen victims in New Jersey, was there to help announce the proposed law, which includes penalties of up to two years imprisonment for sharing harmful images.
“What happened to me and my classmates was not cool, and there’s no way I’m just going to shrug and let it slide,” Mani said. “I’m here, standing up and shouting for change, fighting for laws, so no one else has to feel as lost and powerless as I did on October 20th.”
