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.

我们提出了一种检测篡改参数和位的方法，可以用于检测、定位和恢复已被篡改的参数。我们还提出了一种自适应嵌入方法，以在保持模型准确性的同时最大限度地提高信息容量。我们的方法在多个神经网络上进行了测试，并且当修改率低于 20% 时，结果表明我们的方法在恢复性能方面表现出色。此外，对于水印对准确性产生明显影响的模型，我们利用自适应位技术恢复了超过 15% 的准确性损失。

在深度神经网络中使用自互相检查参数的自适应白盒水印技术

Adaptive White-Box Watermarking with Self-Mutual Check Parameters in  Deep Neural Networks

Compressed Sensing (CS) is a signal processing technique which can accurately
recover sparse signals from linear measurements with far fewer number of
measurements than those required by the classical Shannon-Nyquist theorem.
Block sparse signals, i.e., the sparse signals whose nonzero coefficients occur
in few blocks, arise from many fields. Block orthogonal matching pursuit (BOMP)
is a popular greedy algorithm for recovering block sparse signals due to its
high efficiency and effectiveness. By fully using the block sparsity of block
sparse signals, BOMP can achieve very good recovery performance. This paper
proposes a sufficient condition to ensure that BOMP can exactly recover the
support of block $K$-sparse signals under the noisy case. This condition is
better than existing ones.

本文提出了一种充分利用块稀疏信号块稀疏性的足够条件，确保在噪声情况下使用块正交匹配追踪算法（BOMP）可以准确恢复块 $ K $- 稀疏信号的支撑。