An Effective Imbalanced JPEG Steganalysis Scheme Based on Adaptive Cost-Sensitive Feature Learning

Steganalysis in real-world application often exhibit skewed sample distribution which poses a massive challenge for steganography detection. Conventional steganalysis algorithms are not effective when the training data distribution is imbalanced, and may fail in the scenario of imbalanced data distribution. To address imbalanced data distribution issue in steganalysis, a novel framework termed adaptive cost-sensitive feature learning via F-measure maximization is proposed, which is inspired by the fact that F-measure is a more suitable performance metric compared to accuracy for imbalanced data. We investigate the adaptive cost-sensitive strategy by generating and assigning different weight to each instance with misclassification occurrence. This scheme adaptively determines the weights according to the intra-class and inter-class costs from the imbalanced distribution. Features corresponding to the largest F-measure can be obtained by solving a series of adaptive cost-sensitive feature learning problems with optimization theory. In this way, the learned features are the most representative features between the cover and stego images so that imbalanced steganalysis can significantly alleviate. Extensive experiments on various imbalanced steganalysis tasks show the superiority of the proposed method over the state-of-the-art methods, and it can recognize more minority samples and has excellent classification performance.