# A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models

arXiv: https://arxiv.org/abs/2203.16780 · HTML: https://mlsanity.com/paper/2203.16780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Pyramidal Residual Networks](https://mlsanity.com/paper/1610.02915/) (2016)
- [ShakeDrop Regularization for Deep Residual Learning](https://mlsanity.com/paper/1802.02375/) (2018)
- [Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images](https://mlsanity.com/paper/1811.00236/) (2018)
- [Learnable Image Encryption](https://mlsanity.com/paper/1804.00490/) (2018)
- [Block-wise Scrambled Image Recognition Using Adaptation Network](https://mlsanity.com/paper/2001.07761/) (2020)
- [Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks](https://mlsanity.com/paper/2004.13263/) (2020)
- [A Privacy-Preserving Machine Learning Scheme Using EtC Images](https://mlsanity.com/paper/2007.08775/) (2020)

## Abstract (arXiv)

In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a sequence (as opposed to a single value used in the existing methods) generated by a chaotic map. We have used logistic maps for their low computational requirements. In addition, to compensate for any inefficiency because of the logistic maps, we use a second key to shuffle the sequence. We have compared the proposed method in terms of encryption efficiency and classification accuracy of the DL models on them. We have validated the proposed method with CIFAR datasets. The analysis shows that when classification is performed on the cipher images, the model preserves accuracy of the existing methods while provides better security.