3 ms·

I was curious how big of a deal this would be in a real-world attack (eg could someone use this to DDoS Apple's human reviewers?) so I ran the colliding images

I was curious how big of a deal this would be in a real-world attack (eg could someone use this to DDoS Apple's human reviewers?) so I ran the colliding images through another feature extraction model, OpenAI's CLIP, to see if they also fooled it. They don't; I think it'd be much harder to create an image that both matches the NeuralHash of a CSAM image and also fools a generic model like CLIP as a sanity check for being a CSAM image. I wrote up my findings here: https://blog.roboflow.com/apples-csam-neuralhash-collision/ https://blog.roboflow.com/apples-csam-neuralhash-collision/

dannyw 5y ago
I heard that a lot of content on the CSAM database don't individually look like child porn. For example, a vaginal close up that is of a 17 year old, but could look like a vaginal close up of a 19 year old. Remember, anything under 18 is treated the exact same way from a legal perspective, and once discovered Apple must report. So there is an adversarial attack in that you find legal porn of say 18 year old pussy closeups, disturb it to match CSAM with the neural hash, and send it to your enemies. The Apple employee will verify a match. The Feds will raid your target and imprison them, and reputationally tarnish them for life. Now think about how Pegasus can remotely modify your photos. And think about Julian Assange's sexual assault allegations that were admitted to be fabrications by the accuser.