Energy-Efficient Risk-Constrained Adaptive Semantic Communications Under Uncertain Adversarial Threats

Semantic communication systems based on deep joint source-channel coding can provide task-oriented wireless transmission, but adversarial robustness may require substantial radio and receiver-side defensive-computation energy. This paper develops an energy-efficient, risk-constrained semantic communication framework under source-domain, over-the-air (OTA), and potentially composite adversarial conditions. Long-term total energy, including communication and diffusion-purification energy, is minimized subject to average semantic-loss, trajectory-level tail-risk, and end-to-end latency constraints. A recurrent primal-dual proximal policy optimization controller jointly adapts transmit power, semantic/channel bandwidth ratio, and purification complexity using causal channel observations, semantic confidence, imperfect attack-belief estimates, and recurrent history. A conditional value-at-risk (CVaR) constraint explicitly limits severe semantic failures. On CIFAR-100, at the unseen source-domain level $\epsilon =12\mathrm{/}255$, the proposed controller achieves approximately $61\mathrm{\%}$ robust Top-1 accuracy with ${\mathrm{C}\mathrm{V}\mathrm{a}\mathrm{R}}_{0.9}\approx 0.36$ while using about $9\mathrm{\%}$ less total energy than diffusion-only defense. At the unseen OTA level ${\mathrm{\Gamma }}_A=-5$ dB, it achieves approximately $52\mathrm{\%}$ robust Top-1 accuracy, reduces semantic tail risk by $40\mathrm{\%}$, and consumes about $8\mathrm{\%}$ less total energy than diffusion-only defense. These results demonstrate energy-efficient semantic defense under partially observable adversarial conditions.