VENENA: A Deceptive Visual Encryption Framework for Wireless Semantic Secrecy
Bin Han, Ye Yuan, and Hans D. Schotten

TL;DR
VENENA is an AI-enabled visual encryption framework that actively deceives eavesdroppers in wireless semantic communication, ensuring legitimate receivers accurately recover messages while limiting eavesdropper success.
Contribution
It introduces a practical, system-level visual encryption method for semantic security in wireless communications, combining AI classifiers with superimposed images for active deception.
Findings
Achieves over 93% accuracy for legitimate receivers.
Limits eavesdropper success to 52%.
Validates active defense in 6G semantic systems.
Abstract
Eavesdropping has been a long-standing threat to the security and privacy of wireless communications, since it is difficult to detect and costly to prevent. As networks evolve towards Sixth Generation (6G) and semantic communication becomes increasingly central to next-generation wireless systems, securing semantic information transmission emerges as a critical challenge. While classical physical layer security (PLS) focuses on passive security, the recently proposed concept of physical layer deception (PLD) offers a semantic encryption measure to actively deceive eavesdroppers. Yet the existing studies of PLD have been dominantly information-theoretical and link-level oriented, lacking considerations of system-level design and practical implementation. In this work we propose Visual ENcryption for Eavesdropping NegAtion (VENENA), an artificial intelligence-enabled framework for…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Security in Wireless Sensor Networks · Wireless Communication Security Techniques
