NeuralHash is BROKEN - How to evade Apple's detection & craft hash collisions (w/ Open Source Code)

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Published on ● Video Link: https://www.youtube.com/watch?v=6MUpWGeGMxs



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Duration: 8:16
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#apple #icloud #neuralhash

Send your Apple fanboy friends to prison with this one simple trick ;) We break Apple's NeuralHash algorithm used to detect CSAM for iCloud photos. I show how it's possible to craft arbitrary hash collisions from any source / target image pair using an adversarial example attack. This can be used for many purposes, such as evading detection, or forging false positives, triggering manual reviews.

OUTLINE:
0:00 - Intro
1:30 - Forced Hash Collisions via Adversarial Attacks
2:30 - My Successful Attack
5:40 - Results
7:15 - Discussion

DISCLAIMER: This is for demonstration and educational purposes only. This is not an endorsement of illegal activity or circumvention of law.

Code: https://github.com/yk/neural_hash_collision
Extract Model: https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX
My Video on NeuralHash: https://youtu.be/z15JLtAuwVI

ADDENDUM:
The application of framing people is a bit more intricate than I point out here. Apple has commented that there would be a second perceptual hashing scheme server-side, i.e. the model would not be released, which makes forging false positives harder. Nevertheless, evading the system remains fairly trivial.

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