Towards robust unlearnable examples via deep hiding

Ruohan Meng, Chenyu Yi, Yi Yu, SIYUAN YANG, Bingquan Shen, Alex Kot

Primary Area: societal considerations including fairness, safety, privacy

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Keywords: Data Protection; Information Hiding; Unlearnable Examples

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Abstract: Ensuring data privacy and protection has become paramount in the era of deep learning. Unlearnable examples are proposed to mislead the deep learning models and prevent data from unauthorized exploration by adding small perturbations to data. However, such perturbations (e.g., noise, texture, color change) predominantly impact low-level features, making them vulnerable to countermeasures like adversarial training, data augmentations, and preprocessing. In contrast, semantic images with intricate shapes have a wealth of high-level features, making them more resilient to countermeasures and potential for producing robust unlearnable examples. In this paper, we propose a Deep Hiding (DH) scheme that adaptively hides semantic images enriched with high-level features. We employ an Invertible Neural Network (INN) to invisibly integrate predefined images, inherently hiding them with deceptive perturbations. To enhance data unlearnability, we introduce a Latent Feature Concentration module, designed to work with the INN, regularizing the intra-class variance of these perturbations. To further boost the robustness of unlearnable examples, we design a Semantic Images Generation module that produces hidden semantic images. By utilizing similar semantic information, this module generates similar semantic images for samples within the same classes, thereby enlarging the inter-class distance and narrowing the intra-class distance. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-subset, against 12 countermeasures, reveal that our proposed method exhibits state-of-the-art ro- bustness for unlearnable examples, demonstrating its efficacy in data protection.

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Submission Number: 7150