Progress in cover-source mismatched image steganalysis
Despite the remarkable progress in steganalysis under laboratory settings,the performanceof steganalysis systems often declines in practical applications due to differences in the cover source,steganographic algorithms,and embedding rates between training and testing datasets. This phenom-enon,known as "cover-source mismatch",severely impedes the practical application of steganaly-sis. Therefore,this paper provides an analysis and summary of the main steganalysis methods currently addressing the cover-source mismatch issue. Based on the approach to solving the mismatch issue,existing mismatch steganalysis methods are categorized into three categories:designing training sets,forensics-aided steganalysis,and unsupervised domain adaptation,with each method being reviewed and compared. After comparison,the paper discusses the challenges faced by current deep steganalysis models based on unsupervised domain adaptation and explores future research directions. The research results indicate that deep steganalysis models based on unsupervised domain adaptation are the most effective solution for addressing mismatch issues to date. Domain alignment,intermediate domain bridging,and adversarial learning are the prevailing concepts in designing this type of deep steganalysis model. Introducing fine-grained information,such as class information,to enhance the performance of deep steganalysis models based on unsupervised domain adaptation is a promising di-rection for future research. Further exploration is needed to solve more severe mismatch issues,such as imbalanced samples and single or few shots scenarios.
万方数据
维普
