Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks. (arXiv:2106.10147v1 [cs.CR])

Trigger set-based watermarking schemes have gained emerging attention as they
provide a means to prove ownership for deep neural network model owners. In
this paper, we argue that state-of-the-art trigger set-based watermarking
algorithms do not achieve their designed goal of proving ownership. We posit
that this impaired capability stems from two common experimental flaws that the
existing research practice has committed when evaluating the robustness of
watermarking algorithms: (1) incomplete adversarial evaluation and (2)
overlooked adaptive attacks.

We conduct a comprehensive adversarial evaluation of 10 representative
watermarking schemes against six of the existing attacks and demonstrate that
each of these watermarking schemes lacks robustness against at least two
attacks. We also propose novel adaptive attacks that harness the adversary’s
knowledge of the underlying watermarking algorithm of a target model. We
demonstrate that the proposed attacks effectively break all of the 10
watermarking schemes, consequently allowing adversaries to obscure the
ownership of any watermarked model. We encourage follow-up studies to consider
our guidelines when evaluating the robustness of their watermarking schemes via
conducting comprehensive adversarial evaluation that include our adaptive
attacks to demonstrate a meaningful upper bound of watermark robustness.