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Deep Neural Network Watermarking is a method for provenance verification. A unique, identifying message can be extracted given access to an image classifier. Many watermarking schemes have recently been proposed that claim robustness. We present a systematic, empirical evaluation of these claims against a common, comprehensive set of removal attacks. In total, we survey 31 attacks from the literature and create our own adaptive attacks. Unfortunately, we find that none of the eleven surveyed watermarks are robust in practice, pointing to an intrinsic flaw in how robustness is currently being evaluated. Even more concerning, we find dominant attacks that remove any watermark while maintaining a high utility of the model. You are welcome to join our presentation where you will find out more about deep neural network watermarking, learn about adaptive attacks and defences, and best practices to empirically evaluate robustness.
