A Deep Learning Driven Feature Based Steganalysis Approach

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this pro-blem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic algorithms can obtain diverse features of each algorithm. Then a multi-classifier implemented as lightgbm is used to predict the matching algorithm. Experimental results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.