Adversarial subdomain adaptation network for mismatched steganalysis - Lei ZHANG; Hongxia WANG (网络与信息安全学报)
Once data in the training and test sets come from different cover sources, that is, under the condition of cover source mismatch, it usually makes the detection accuracy rate of an outstanding steganalysis model to be reduced.In practical applications, the analyzers need to process images collected from the Internet.However, compared with the training set data, these suspicious images are likely to have completely different capture and processing histories, which may lead to the degradation of steganalysis model.It is also why steganalysis tools are difficult to deploy successfully in the real-world applications.To improve the practical application value of steganalysis methods based on deep learning, test sample information is utilized and domain adaptation method is used to solve the problem of cover source mismatch.Regarding the training set data as the source domain and test set data as the target domain, the detection performance of steganalysis models in the target domain is enhanced by minimizing the discrepancy between the feature distribution of source domain and target domain.ASAN (adversarial subdomain adaptation network) was proposed from the perspective of feature generation on the one hand.The source domain features and target domain features generated by the steganalysis model were required to be as similar as possible, so that the discriminator cannot distinguish which domain the features came from.On the other hand, to reduce the difference of feature distribution between domains, the subdomain adaptation method was adopted to reduce the unexpected change of the distribution of related subdomains.The distance between the cover and stego samples was enlarged effectively to improve the classification accuracy.After testing three steganography algorithms on multiple datasets, it is confirmed that the proposed method can effectively improve the detection accuracy rate of the model in the case of dataset mismatch and algorithm mismatch and it can also reduce the negative impact of the mismatch problem of the model.