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arXiv · 2202.08602

Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

Zirui Peng·Shaofeng Li·Guoxing Chen·Cheng Zhang·Haojin Zhu·Minhui Xue
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Abstract

In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive learning that takes fingerprint as inputs, outputs a similarity score. Extensive studies show that our framework can detect model IP breaches with confidence > 99.99 within only 20 fingerprints of the suspect model. It has good generalizability across different model architectures and is robust against post-modifications on stolen models.

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Zirui Peng, Shaofeng Li, Guoxing Chen, Cheng Zhang, Haojin Zhu, Minhui Xue. 2022-02-17. Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations. https://arxiv.org/abs/2202.08602

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