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Deep Neural Network Watermarking is a method for provenance verification through an identifying message extracted by an image classifier. Recent watermarking schemes claim robustness, but a systematic evaluation of 31 attacks reveals none of the surveyed watermarks are actually robust. Dominant attacks can remove watermarks while maintaining model utility, highlighting flaws in current evaluation methods. Join the presentation to learn more about deep neural network watermarking, adaptive attacks and defenses, and best practices for evaluating robustness.
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