A Robust Integrity Verification and Recovery Framework for Coarse-to-Fine Text Semantic Communication Against Model Tampering Attacks

This paper proposes the first integrity verification and recovery framework for text semantic communication systems that combines component-level tamper detection, localization, and automatic recovery. Using cryptographic fingerprints and digital signatures, the framework verifies receiver model integrity, detects unauthorized tampering, identifies the affected component, and restores it from a trusted backup. Experiments under AWGN and Rayleigh fading channels demonstrate 100% detection and localization accuracy, with recovery completed in about 96 ms while maintaining reliable semantic communication performance.