Image selective encryption analysis using mutual information in CNN based embedding space
It addresses privacy concerns in image data transmission by proposing a method for leakage detection, but it appears incremental as it applies existing estimators to a specific domain without new breakthroughs.
This work tackled the problem of detecting information leakage in selectively encrypted images by using mutual information estimators, such as the empirical estimator and MINE framework, to analyze leakage in CNN-based embedding spaces, though no concrete numerical results are provided.
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.