# Grayscale-based Block Scrambling Image Encryption for Social Networking Services

arXiv: https://arxiv.org/abs/1806.03787 · HTML: https://mlsanity.com/paper/1806.03787/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2303.05036/) (2023)
- [A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique](https://mlsanity.com/paper/2507.12730/) (2025)
- [A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models](https://mlsanity.com/paper/2301.03843/) (2023)
- [Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer](https://mlsanity.com/paper/2301.09255/) (2023)
- [Privacy-Preserving Vision Transformer Using Images Encrypted with Restricted Random Permutation Matrices](https://mlsanity.com/paper/2408.08529/) (2024)
- [Privacy-Preserving Object Detection for Vision Transformer-Based Models](https://mlsanity.com/paper/2608.20712/) (2026)

## Abstract (arXiv)

This paper proposes a new block scrambling encryption scheme that enhances the security of encryption-then-compression (EtC) systems for JPEG images, which are used, for example, to securely transmit images through an untrusted channel provider. The proposed method allows the use of a smaller block size and a larger number of blocks than the conventional ones. Moreover, images encrypted using proposed scheme include less color information due to the use of grayscale even when the original image has three color channels. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. The results of an experiment in which encrypted images were uploaded to and then downloaded from Twitter and Facebook demonstrated the effectiveness of the proposed scheme for EtC systems.