Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

IEEE Conference on Computer Vision and Pattern Recognition(2022)

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摘要
In this paper, we propose a novel and practical mechanism to enable 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 fingerprints as inputs, outputs a similarity score. Extensive studies show that our framework can detect model Intellectual Property (IP) breaches with confidence > 99.99 % within only 20 fingerprints of the suspect model. It also has good generalizability across different model architectures and is robust against post-modifications on stolen models.
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关键词
Transparency,fairness,accountability,privacy and ethics in vision, Machine learning, Self-& semi-& meta- & unsupervised learning
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