A Child's Nightmare Realized: How AI is Reviving the Fear of Stranger Danger
For many, the phrase "Stranger Danger" evokes memories of childhood safety drills and warnings about the dangers of talking to strangers. However, for Mara Wilson, a former child actor, the fear of stranger danger has taken on a new, disturbing form with the rise of generative AI.
In the late 1980s and early 1990s, kids were taught to be wary of strangers, but for Wilson, it was her own image that became a target for child sexual abuse material (CSAM) creators. Her face was featured on fetish websites, and she received creepy letters from men who had Photoshopped her into pornography.
Fast forward to today, and the use of generative AI has made it infinitely easier for child predators to create CSAM. A recent study found over 3,500 images of AI-generated CSAM on a dark web forum, with many more likely created in the year and a half since then.
The technology behind AI-powered CSAM creation is complex, but the principle remains the same: by training AI models on existing datasets, they can learn to generate realistic images that mimic real people. This means that even children whose faces have been posted online are at risk of being exploited.
To combat this threat, experts say that looking at how AI is trained is key. Generative AI "learns" by a process of repeated comparison and updating, which creates models based on patterns it has memorized. However, this also means that if an AI is trained on existing CSAM, it can learn to replicate the images.
The issue is compounded by the lack of regulation around AI-generated content. Some companies claim to have safeguards in place, but others are pushing for more open-source models, which could enable even more creators to access and exploit child images.
In response, some countries have enacted laws requiring AI content to be labelled as such. Denmark is working on legislation that would give citizens the copyright to their appearances and voices, while in other parts of Europe, people's images may be protected by General Data Protection Regulation (GDPR).
However, the outlook in the US appears grim. Copyright claims won't be enough to protect children, and with executive orders against regulating generative AI, it seems that making money with AI is prioritized over keeping citizens safe.
The solution lies not just with legislation but also with technological solutions. Experts are working on tools that can detect and notify people when their images or creative work are being scraped.
For many, the fear of stranger danger has always been about protecting children from harm. But in the age of AI-powered CSAM, it's no longer just about strangers β it's about anyone who can create and share realistic images of real people online.
To combat this threat, we need to demand that companies be held accountable for enabling CSAM creation. We need legislation and technological safeguards, and most importantly, we need to take responsibility as parents and caregivers to protect our children from the risks of the internet.
For many, the phrase "Stranger Danger" evokes memories of childhood safety drills and warnings about the dangers of talking to strangers. However, for Mara Wilson, a former child actor, the fear of stranger danger has taken on a new, disturbing form with the rise of generative AI.
In the late 1980s and early 1990s, kids were taught to be wary of strangers, but for Wilson, it was her own image that became a target for child sexual abuse material (CSAM) creators. Her face was featured on fetish websites, and she received creepy letters from men who had Photoshopped her into pornography.
Fast forward to today, and the use of generative AI has made it infinitely easier for child predators to create CSAM. A recent study found over 3,500 images of AI-generated CSAM on a dark web forum, with many more likely created in the year and a half since then.
The technology behind AI-powered CSAM creation is complex, but the principle remains the same: by training AI models on existing datasets, they can learn to generate realistic images that mimic real people. This means that even children whose faces have been posted online are at risk of being exploited.
To combat this threat, experts say that looking at how AI is trained is key. Generative AI "learns" by a process of repeated comparison and updating, which creates models based on patterns it has memorized. However, this also means that if an AI is trained on existing CSAM, it can learn to replicate the images.
The issue is compounded by the lack of regulation around AI-generated content. Some companies claim to have safeguards in place, but others are pushing for more open-source models, which could enable even more creators to access and exploit child images.
In response, some countries have enacted laws requiring AI content to be labelled as such. Denmark is working on legislation that would give citizens the copyright to their appearances and voices, while in other parts of Europe, people's images may be protected by General Data Protection Regulation (GDPR).
However, the outlook in the US appears grim. Copyright claims won't be enough to protect children, and with executive orders against regulating generative AI, it seems that making money with AI is prioritized over keeping citizens safe.
The solution lies not just with legislation but also with technological solutions. Experts are working on tools that can detect and notify people when their images or creative work are being scraped.
For many, the fear of stranger danger has always been about protecting children from harm. But in the age of AI-powered CSAM, it's no longer just about strangers β it's about anyone who can create and share realistic images of real people online.
To combat this threat, we need to demand that companies be held accountable for enabling CSAM creation. We need legislation and technological safeguards, and most importantly, we need to take responsibility as parents and caregivers to protect our children from the risks of the internet.