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

arXiv: https://arxiv.org/abs/2508.08832 · HTML: https://mlsanity.com/paper/2508.08832/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Reflections on Shannon Information: In search of a natural information-entropy for images](https://mlsanity.com/paper/1609.01117/) (2016)
- [CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation](https://mlsanity.com/paper/2309.08019/) (2023)
- [Edge Detectors Can Make Deep Convolutional Neural Networks More Robust](https://mlsanity.com/paper/2402.16479/) (2024)
- [Combining Forensics and Privacy Requirements for Digital Images](https://mlsanity.com/paper/2103.03569/) (2021)

## Built on by (most cited first, 1 of 1)

- [Semantic Leakage from Image Embeddings](https://mlsanity.com/paper/2601.22929/) (2026)

## Abstract (arXiv)

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.