Spatial Steganalysis Based on Gradient-Based Neural Architecture Search

Xiaoqing Deng, Weiqi Luo, Yanmei Fang

Abstract: Most existing steganalytic networks are designed empirically, which probably limits their performances. Neural architecture search (NAS) is a technology that can automatically find the optimal network architecture in the search space without excessive manual intervention. In this paper, we introduce a gradient-based NAS method called PC-DARTS in steganalysis. We firstly define the overall network architecture, and the search spaces of the corresponding cells in the network. We then use softmax over all candidate operations to construct an over-parameterized network. By updating the parameters of such a network based on gradient descent, the optimal operations, i.e., the high-pass filters in pre-processing module and operations in feature extraction module, can be obtained. Experimental results show that the resulting steganalytic network via NAS can achieve competitive performance with some advanced well-designed steganalytic networks, while the searching time is relatively short.