Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise. (arXiv:2311.13091v1 [cs.LG])

The open source of large amounts of image data promotes the development of
deep learning techniques. Along with this comes the privacy risk of these
open-source image datasets being exploited by unauthorized third parties to
train deep learning models for commercial or illegal purposes. To avoid the
abuse of public data, a poisoning-based technique, the unlearnable example, is
proposed to significantly degrade the generalization performance of models by
adding a kind of imperceptible noise to the data. To further enhance …

commercial data datasets deep learning development error exploited illegal image large noise open source privacy privacy risk risk robustness techniques third third parties train