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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.08832 |
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| _version_ | 1866911102844010496 |
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| author | Messadi, Ikram Cervia, Giulia Itier, Vincent |
| author_facet | Messadi, Ikram Cervia, Giulia Itier, Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08832 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Image selective encryption analysis using mutual information in CNN based embedding space Messadi, Ikram Cervia, Giulia Itier, Vincent Cryptography and Security Information Theory Machine Learning 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. |
| title | Image selective encryption analysis using mutual information in CNN based embedding space |
| topic | Cryptography and Security Information Theory Machine Learning |
| url | https://arxiv.org/abs/2508.08832 |