{"ID":2883204,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08832","arxiv_id":"2508.08832","title":"Image selective encryption analysis using mutual information in CNN based embedding space","abstract":"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.","short_abstract":"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 g...","url_abs":"https://arxiv.org/abs/2508.08832","url_pdf":"https://arxiv.org/pdf/2508.08832v1","authors":"[\"Ikram Messadi\",\"Giulia Cervia\",\"Vincent Itier\"]","published":"2025-08-12T10:39:31Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.IT\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}