Re-thinking Data Availablity Attacks Against Deep Neural Networks
Bin Fang Bo Li Shuang Wu Ran Yi Shouhong Ding Lizhuang Ma
The unauthorized use of personal data for commercial purposes and the
clandestine acquisition of private data for training machine learning models
continue to raise concerns. In response to these issues, researchers have
proposed availability attacks that aim to render data unexploitable. However,
many current attack methods are rendered ineffective by adversarial training.
In this paper, we re-examine the concept of unlearnable examples and discern
that the existing robust error-minimizing noise presents an inaccurate
optimization objective. Building on these observations, we introduce a novel
optimization paradigm that yields improved protection results with reduced
computational time requirements. We have conducted extensive experiments to
substantiate the soundness of our approach. Moreover, our method establishes a
robust foundation for future research in this area.