In modern AI environments, deep learning models have become valuable digital assets that are vulnerable to tampering and malicious parameter manipulation. Once a model is compromised, restoring its integrity usually requires costly and time-consuming retraining. Existing protection methods mainly focus on detecting tampering or embedding fragile watermarks, but they lack effective mechanisms for recovering the original model parameters. Large-scale or subtle perturbations present additional challenges, as traditional watermarking designs often fail under such conditions. Therefore, there is a growing need for a unified and verifiable recovery framework that can both locate  and restore model tampered parameters efficiently, even when a significant portion of the parameters has been altered.