Grayscale-based block scrambling image encryption using YCbCr color space for encryption-then-compression systems

Abstract

A novel grayscale-based block scrambling image encryption scheme is presented not only to enhance security, but also to improve the compression performance for Encryption-then-Compression (EtC) systems with JPEG compression, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the color-based image encryption scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images 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. Moreover, generating the grayscale-based images from a full-color image in YCbCr color space allows the use of color sub-sampling operation, which can provide the higher compression performance than the conventional grayscale-based encryption scheme, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Twitter and Facebook, and the results demonstrated that the proposed scheme is effective for EtC systems and enhances the compression performance, while maintaining the security against brute-force and jigsaw puzzle solver attacks.

Privacy-Preserving Deep Neural Networks with Pixel-Based Image Encryption Considering Data Augmentation in the Encrypted Domain

“…Compared with full encryption with provable security like homomorphic encryption (HE), they generally have a low computational cost and can offer encrypted data robust against various kinds of noise and errors. In addition, some of them aim to consider both security and efficient compression so that they can be adapted to cloud storage and network sharing [9][10][11][12][13][14][15]. However, with the exception of a few previous pieces of work, most conventional perceptual encryption methods have never been considered for application to machine learning algorithms [5,6].…”

“…A few methods were applied to machine learning algorithms in previous works [5,6]. The first encryption method [9][10][11][12][13][14] to be proposed for encryption-then-compression (EtC) systems, was demonstrated to be applicable to traditional machine learning algorithms, such as support vector machine (SVM) [5]. However, the block-based encryption method has never been applied to DNNs.…”

An Image Identification Scheme Of Encrypted Jpeg Images For Privacy-Preserving Photo Sharing Services

“…We focus on EtC images which have been proposed for Encryptionthen-Compression (EtC) systems with JPEG compression [6][7][8][9][10]. EtC images have not only almost the same compression performance as that of unencrypted images, but also enough robustness again various ciphertext-only attacks including jigsaw puzzle solver attacks [8].…”

“…However, most schemes do not consider requirement 2, although requirement 3 is considered in several schemes [13][14][15][16][17]. As systems that satisfy both requirements 1 and 2, Encryption-then-Compression (EtC) systems have been developed [6][7][8][9][10]. In this paper, we focus on a blockscrambling-based image encryption method that has been proposed for EtC systems.…”

“…Thus, we propose a novel image identification scheme for encrypted JPEG images compressed under various coding conditions. Image encryption is carried out by extending the method based on block-scrambling for EtC systems [6][7][8][9][10]. The extended method has steps for two-layer encryption.…”