Model Copyright Protection in Buyer-seller Environment
Yusheng Guo Nan Zhong Zhenxing Qian Xinpeng Zhang
Training a deep neural network (DNN) requires a high computational cost.
Buying models from sellers with a large number of computing resources has
become prevailing. However, the buyer-seller environment is not always trusted.
To protect the neural network models from leaking in an untrusted environment,
we propose a novel copyright protection scheme for DNN using an input-sensitive
neural network (ISNN). The main idea of ISNN is to make a DNN sensitive to the
key and copyright information. Therefore, only the buyer with a correct key can
utilize the ISNN. During the training phase, we add a specific perturbation to
the clean images and mark them as legal inputs, while the other inputs are
treated as illegal input. We design a loss function to make the outputs of
legal inputs close to the true ones, while the illegal inputs are far away from
true results. Experimental results demonstrate that the proposed scheme is
effective, valid, and secure.