Calibration-based Steganalysis for Neural Network Steganography

Recent research has shown that neural network models can be used to steal sensitive data or embed malware. Therefore, steganalysis for neural networks is urgently needed. However, existing neural network steganalysis methods do not perform well under small embedding rates. In addition, because of the large number of parameters, the neural network steganography method under a small embedding rate can embed enough information into the model for malicious purposes. To address this problem, this paper proposes a calibration-based steganalysis method, which fine-tunes the original neural network model without implicit constraints to obtain a reference model, then extracts and fuses statistical moments from the parameter distributions of the original model and its reference model, and finally trains a logistic regressor for detection. Extensive experiments show that the proposed method has superior performance in detecting steganographic neural network models under small embedding rates.