Encryption Resistant Deep Neural Network Watermarking

Guobiao Li, Sheng Li, Zhenxing Qian, Xinpeng Zhang

Abstract: Deep neural network (DNN) watermarking is one of the main techniques to protect the DNN. Although various DNN watermarking schemes have been proposed, none of them is able to resist the DNN encryption. In this paper, we propose an encryption resistent DNN watermarking scheme, which is able to resist the parameter shuffling based DNN encryption. Unlike the existing schemes which use the kernels separately for watermarking embedding, we propose to embed the watermark into the fused kernels to resist the parameter shuffling. We further propose a MappingNet to map the the fused kernels into a higher dimension to increase the watermarking capacity. The MappingNet and the DNN are jointly trained to conduct final watermark embedding. Experimental results indicate the effectiveness of our proposed scheme for resisting the DNN encryption.