Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission

TL;DR

This paper introduces a deep neural network-based joint source-channel coding scheme for privacy-aware image transmission that effectively balances image quality and privacy against multiple eavesdroppers under real-world unknown conditions.

Contribution

It proposes a novel multi-objective optimization framework for privacy-aware image transmission that handles unknown, non-i.i.d. source and channel conditions, improving privacy and quality.

Findings

Significant SSIM and adversarial accuracy improvements

Effective privacy protection against colluding and non-colluding eavesdroppers

Robust performance under unknown source and channel statistics

Abstract

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, the proposed scheme operates under unknown and non-i.i.d. conditions, making it more applicable to real-world scenarios. The goal is to transmit images with minimum distortion, while simultaneously preventing eavesdroppers from inferring certain private attributes of images. Simultaneously generalizing the ideas of privacy funnel and wiretap coding, a multi-objective optimization framework is expressed that characterizes the tradeoff between image reconstruction quality and information leakage to eavesdroppers, taking into account the structural similarity index (SSIM) for improving the perceptual quality of image reconstruction. Extensive experiments on the CIFAR-10 and CelebA, along with ablation studies, demonstrate significant performance improvements in terms of SSIM, adversarial accuracy, and the mutual information leakage compared to benchmarks. Experiments show that the proposed scheme restrains the adversarially-trained eavesdroppers from intercepting privatized data for both cases of eavesdropping a common secret, as well as the case in which eavesdroppers are interested in different secrets. Furthermore, useful insights on the privacy-utility trade-off are also provided.