Abstract: Artificial Intelligence (AI) models are valuable and frequently face malicious tampering attacks, which require significant data and time to retrain when compromised. To address this issue, we propose a Guarded High-fidelity Compression-based Watermarking (GHCW) scheme for detecting and further recovering the tampered parameters, aiming to protect the model’s functional performance without the necessity for retraining. In GHCW, the watermark consists of an authentication probe for tamper detection, recovery bits, and ciphertext for model recovery. The authentication probe is generated by computing inherent characteristics of the parameters using hash algorithms and eigenvalue calculations. Different from existing works, the recovery bits are produced through a high-fidelity model compression technique, which concentrates key model information into fewer high-priority bits. This design enables the recovery mechanism to remain effective even under large-scale parameter tampering. To protect these bits, they are linearly encrypted with a key, generating a ciphertext that serves as a guard. To the best of our knowledge, we are one of the first to propose and implement this idea in the area of AI model protection. Experimental results demonstrate that GHCW excels in recovering models from tampering, especially large-scale parameter disturbance. Compared to existing methods, GHCW shows superiority in both model recovery and tolerance to tampering rate, tolerating tampering rates up to 70%, while existing methods recover at most 50%.