Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission
Journal:
arXiv
Published Date:
Dec 22, 2024
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.
