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This is a Quantitative Study study (also classified as: Experimental Study).

October 2, 2025Open Access

Diffusion-based Task-oriented Semantic Communications with Model Inversion Attack

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DiffSem improves classification accuracy by 10.03% on the MNIST dataset, suggesting enhanced task performance.
Proposed new metric quantifies semantic fidelity, providing a more reliable assessment than traditional image quality indicators.
DiffSem uses a diffusion mechanism and self-referential label embedding to improve robustness against noise in various environments.
Traditional methods may inadequately assess task-oriented semantic communication, highlighting the need for improved evaluation techniques.

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Proposed diffusion-based semantic communication framework optimizes task performance in 6G networks while mitigating model inversion attack risks.

Wang et al. (2025) studied this question.

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Proposed diffusion-based semantic communication framework optimizes task performance in 6G networks while mitigating model inversion attack risks.