Ungeneralizable Examples
CVPR 2024(2024)
摘要
The training of contemporary deep learning models heavily relies on publicly
available data, posing a risk of unauthorized access to online data and raising
concerns about data privacy. Current approaches to creating unlearnable data
involve incorporating small, specially designed noises, but these methods
strictly limit data usability, overlooking its potential usage in authorized
scenarios. In this paper, we extend the concept of unlearnable data to
conditional data learnability and introduce UnGeneralizable
Examples (UGEs). UGEs exhibit learnability for authorized users while
maintaining unlearnability for potential hackers. The protector defines the
authorized network and optimizes UGEs to match the gradients of the original
data and its ungeneralizable version, ensuring learnability. To prevent
unauthorized learning, UGEs are trained by maximizing a designated distance
loss in a common feature space. Additionally, to further safeguard the
authorized side from potential attacks, we introduce additional undistillation
optimization. Experimental results on multiple datasets and various networks
demonstrate that the proposed UGEs framework preserves data usability while
reducing training performance on hacker networks, even under different types of
attacks.
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