Steganalysis of neural networks using implicit features

Abstract Deep neural networks are used increasingly in daily life. Many models contain redundant parameters that render them susceptible to being exploited for transmitting secret data, thereby injecting malware or stealing sensitive data. Research on steganographic schemes for neural networks is therefore necessary. Existing steganalysis methods often require access to network parameters, while black-box approaches achieve limited accuracy. In this work, we propose a black-box steganalysis scheme that feeds a fixed sequence of images into the target network to extract implicit features, which are then used to train a steganalysis network. This is a black-box steganalysis framework without access to internal network parameter, leveraging output probabilities from fixed image sequences to capture model behavior and enabling steganography detection across diverse network architectures. Experiments show that our method improves accuracy by 6–39.65% compared to existing steganalysis schemes.