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The design of an effective multi-bit watermarking algorithm hinges uponfinding a good trade-off between the three fundamental requirements forming thewatermarking trade-off triangle, namely, robustness against networkmodifications, payload, and unobtrusiveness, ensuring minimal impact on theperformance of the watermarked network. In this paper, we first revisit thenature of the watermarking trade-off triangle for the DNN case, then we exploitour findings to propose a white-box, multi-bit watermarking method achievingvery large payload and strong robustness against network modification. In theproposed system, the weights hosting the watermark are set prior to training,making sure that their amplitude is large enough to bear the target payload andsurvive network modifications, notably retraining, and are left unchangedthroughout the training process. The distribution of the weights carrying thewatermark is theoretically optimised to ensure the secrecy of the watermark andmake sure that the watermarked weights are indistinguishable from thenon-watermarked ones. The proposed method can achieve outstanding performance,with no significant impact on network accuracy, including robustness againstnetwork modifications, retraining and transfer learning, while ensuring apayload which is out of reach of state of the art methods achieving a lower -or at most comparable - robustness.

Robust and Large-Payload DNN Watermarking via Fixed, Distribution-Optimized, Weights