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
Online-Zugang:	Volltext
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