Ungeneralizable Examples

Jingwen Ye, Xinchao Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 11944-11953

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.