用互信息检测图像选择性加密中的信息泄露，为隐私保护提供量化依据。
Image selective encryption analysis using mutual information in CNN based embedding space
- 基于CNN嵌入空间，采用互信息估计器分析加密图像的信息泄露
- 实证表明，加密后仍存在可被估算的残余信息，揭示安全漏洞
- 适合关注图像隐私与深度学习安全的研究者
随着数字数据传输规模持续扩大，隐私问题日益紧迫，但隐私仍是社会建构且定义模糊的概念，缺乏普遍接受的量化度量。本文研究图像数据中的信息泄露问题，该领域尚缺乏信息论层面的严谨保障。在深度学习、信息论与密码学交叉领域，我们探究互信息（MI）估计器（特别是经验估计器和MINE框架）在检测选择性加密图像中泄露信息的应用。基于一个核心直觉：鲁棒的估计器需具备捕捉空间依赖性和加密表示中残余结构的概率框架。本工作为图像信息泄露的量化评估提供了有前景的方向。
原文摘要 · Abstract (English)
As digital data transmission continues to scale, concerns about privacy grow increasingly urgent - yet privacy remains a socially constructed and ambiguously defined concept, lacking a universally accepted quantitative measure. This work examines information leakage in image data, a domain where information-theoretic guarantees are still underexplored. At the intersection of deep learning, information theory, and cryptography, we investigate the use of mutual information (MI) estimators - in particular, the empirical estimator and the MINE framework - to detect leakage from selectively encrypted images. Motivated by the intuition that a robust estimator would require a probabilistic frameworks that can capture spatial dependencies and residual structures, even within encrypted representations - our work represent a promising direction for image information leakage estimation.
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