Encryption-Then-Compression Systems Using Grayscale-Based Image Encryption for JPEG Images

Abstract

A block scrambling-based encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems with JPEG compression, which allow us to securely transmit images through an untrusted channel provider, such as social network service providers. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the conventional 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. In an experiment, the security against jigsaw puzzle solver attacks is evaluated. Encrypted images were uploaded to and then downloaded from Facebook and Twitter, and the results demonstrated that the proposed scheme is effective for EtC systems.

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].…”

“…p is the pixel value of the original image with L bit per pixel. The value of the occurrence probability P (r(i)) = 0.5 is used to invert bits randomly [13]. 3) (Optional) Shuffle three color components of each pixel by using an integer randomly selected from six integers generated by a key Ks as shown in Table 1.…”

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“…p is the pixel value of the original image with L bit per pixel. The value of the occurrence probability P (r(j)) = 0.5 is used to invert bits randomly [13]. 3) (Optional) Shuffle three color components of each pixel by using an integer randomly selected from six integers generated by a key K s,i as shown in Table I.…”

Privacy-Preserving Image Sharing Via Sparsifying Layers on Convolutional Groups

“…While there is an extensive literature on using traditional image compression codecs to provide security along with compression, (e.g., see [12][13][14][15]), an important limitation arises with these approaches when they are used in largescales and possibly for domain-specific images. Since in such scenarios images have similar encoding (e.g., high concentration of activation of DCT coefficients at certain regions), the adversary can benefit from this to infer the statistics of encoded images.…”