DeepDIST: a black-box anti-collusion framework for secure distribution of deep models - Cheng, Hang (2023)
Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.