论文标题
深神经网络的扩展转换抖动调制水印
Spread-Transform Dither Modulation Watermarking of Deep Neural Network
论文作者
论文摘要
DNN水印正受到越来越多的关注，作为保护与DNN模型相关的知识产权的合适含义。到目前为止，提出的几种方法受到流行的扩散光谱（SS）范式的启发，根据该范围，将水印位嵌入DNN模型重量的投影到伪随机序列上。在本文中，我们提出了一种新的DNN水印算法，该算法利用侧面信息范式在水印上利用，以降低水印的掩盖性并增加其有效载荷。特别是，新方案利用了ST-DM（传播变换调制）水印的主要思想，以提高基于常规SS的最近提出的算法的性能。我们通过将提出的方案应用于水印不同模型进行的实验表明，其能力提供了比基于常规SS的基线方法更高的有效载荷，同时保留了令人满意的鲁棒性水平。
DNN watermarking is receiving an increasing attention as a suitable mean to protect the Intellectual Property Rights associated to DNN models. Several methods proposed so far are inspired to the popular Spread Spectrum (SS) paradigm according to which the watermark bits are embedded into the projection of the weights of the DNN model onto a pseudorandom sequence. In this paper, we propose a new DNN watermarking algorithm that leverages on the watermarking with side information paradigm to decrease the obtrusiveness of the watermark and increase its payload. In particular, the new scheme exploits the main ideas of ST-DM (Spread Transform Dither Modulation) watermarking to improve the performance of a recently proposed algorithm based on conventional SS. The experiments we carried out by applying the proposed scheme to watermark different models, demonstrate its capability to provide a higher payload with a lower impact on network accuracy than a baseline method based on conventional SS, while retaining a satisfactory level of robustness.
