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 common
countermeasures. 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 an
ImageNet subset, against 18 countermeasures, reveal that our proposed method
exhibits outstanding robustness for unlearnable examples, demonstrating its
efficacy in preventing unauthorized data exploitation.

在确保数据隐私和保护的时代，我们提出了一种深度隐藏方案，适应性地隐藏富含高级特征的语义图像，通过不可见地集成预定义图像并结合欺骗性扰动隐藏它们，以增强数据的不可学习性和阻止未经授权的数据探索。