Image selective encryption analysis using mutual information in CNN based embedding space

--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. Images are among the most common forms of data shared online, and with the widespread use of cloud storage, users frequently upload images to the web. Regardless of content sensitivity, image privacy remains a critical concern.