On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks

Obtaining the state of the art performance of deep learning models imposes a\nhigh cost to model generators, due to the tedious data preparation and the\nsubstantial processing requirements. To protect the model from unauthorized\nre-distribution, watermarking approaches have been introduced in the past\ncouple of years. We investigate the robustness and reliability of\nstate-of-the-art deep neural network watermarking schemes. We focus on\nbackdoor-based watermarking and propose two -- a black-box and a white-box --\nattacks that remove the watermark. Our black-box attack steals the model and\nremoves the watermark with minimum requirements; it just relies on public\nunlabeled data and a black-box access to the classification label. It does not\nneed classification confidences or access to the model's sensitive information\nsuch as the training data set, the trigger set or the model parameters. The\nwhite-box attack, proposes an efficient watermark removal when the parameters\nof the marked model are available; our white-box attack does not require access\nto the labeled data or the trigger set and improves the runtime of the\nblack-box attack up to seventeen times. We as well prove the security\ninadequacy of the backdoor-based watermarking in keeping the watermark\nundetectable by proposing an attack that detects whether a model contains a\nwatermark. Our attacks show that a recipient of a marked model can remove a\nbackdoor-based watermark with significantly less effort than training a new\nmodel and some other techniques are needed to protect against re-distribution\nby a motivated attacker.\n