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

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 ident...

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Veröffentlicht in:IEEE transactions on machine learning in communications and networking Jg. 3; S. 568 - 584
Hauptverfasser: Letafati, Mehdi, Amirhossein Ameli Kalkhoran, Seyyed, Erdemir, Ecenaz, Hossein Khalaj, Babak, Behroozi, Hamid, Gunduz, Deniz
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
adversarial neural networks
Autoencoders
Channel coding
Communication system security
deep learning
DeepJSCC
Eavesdropping
end-to-end learning
Image communication
Image reconstruction
privacy-utility trade-off
secure image transmission
Security
Training
Wireless networks
Wireless sensor networks
ISSN:2831-316X, 2831-316X
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Zusammenfassung: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.
ISSN:2831-316X
2831-316X
DOI:10.1109/TMLCN.2025.3564907