Deep joint source channel coding for privacy-aware end-to-end image transmission
File(s)
Author(s)
Letafati, Mehdi
Amirhossein Ameli Kalkhoran, Seyyed
Erdemir, Ecenaz
Hossein Khalaj, Babak
Behroozi, Hamid
Type
Journal Article
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 trade-off 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.
Date Issued
2025-01-01
Date Acceptance
2025-04-22
Citation
IEEE Transactions on Machine Learning in Communications and Networking, 2025, 3, pp.568-584
ISSN
2831-316X
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
568
End Page
584
Journal / Book Title
IEEE Transactions on Machine Learning in Communications and Networking
Volume
3
Copyright Statement
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
10.1109/TMLCN.2025.3564907
Publication Status
Published
Date Publish Online
2025-04-28