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Deep Neural Network Watermarking is a method for provenance verification, where a unique message can be extracted with access to an image classifier. Multiple watermarking schemes claim robustness, but a systematic evaluation against removal attacks shows none are truly robust. This points to flaws in current evaluation methods. Dominant attacks exist that remove watermarks while maintaining model utility. A presentation will provide more information on deep neural network watermarking, adaptive attacks, defenses, and best practices for evaluating robustness.
