  Artificial Intelligence (AI) has found wide application, but also poses risks
due to unintentional or malicious tampering during deployment. Regular checks
are therefore necessary to detect and prevent such risks. Fragile watermarking
is a technique used to identify tampering in AI models. However, previous
methods have faced challenges including risks of omission, additional
information transmission, and inability to locate tampering precisely. In this
paper, we propose a method for detecting tampered parameters and bits, which
can be used to detect, locate, and restore parameters that have been tampered
with. We also propose an adaptive embedding method that maximizes information
capacity while maintaining model accuracy. Our approach was tested on multiple
neural networks subjected to attacks that modified weight parameters, and our
results demonstrate that our method achieved great recovery performance when
the modification rate was below 20%. Furthermore, for models where watermarking
significantly affected accuracy, we utilized an adaptive bit technique to
recover more than 15% of the accuracy loss of the model.
