Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring

Authors: 

Yossi Adi and Carsten Baum, Bar Ilan University; Moustapha Cisse, Google Inc; Benny Pinkas and Joseph Keshet, Bar Ilan University

Abstract: 

Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expensive and requires vast amounts of training data. Selling such pre-trained models can, therefore, be a lucrative business model. Unfortunately, once the models are sold they can be easily copied and redistributed. To avoid this, a tracking mechanism to identify models as the intellectual property of a particular vendor is necessary. In this work, we present an approach for watermarking Deep Neural Networks in a black-box way. Our scheme works for general classification tasks and can easily be combined with current learning algorithms. We show experimentally that such a watermark has no noticeable impact on the primary task that the model is designed for and evaluate the robustness of our proposal against a multitude of practical attacks. Moreover, we provide a theoretical analysis, relating our approach to previous work on backdooring.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {217591,
author = {Yossi Adi and Carsten Baum and Moustapha Cisse and Benny Pinkas and Joseph Keshet},
title = {Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring},
booktitle = {27th USENIX Security Symposium (USENIX Security 18)},
year = {2018},
isbn = {978-1-939133-04-5},
address = {Baltimore, MD},
pages = {1615--1631},
url = {https://www.usenix.org/conference/usenixsecurity18/presentation/adi},
publisher = {USENIX Association},
month = aug
}

Presentation Video 

Presentation Audio