Deep-Learning Image Steganalysis Based on Generalized Gaussian Distribution Features Clustering

Deep-Learning Image Steganalysis Based on Generalized Gaussian Distribution Features Clustering

연구 분야: Strategies

학회: 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

The difference in the statistical distribution between the training images and the test images reduces the performance of the steganalysis model, and this sample mismatch phenomenon makes it difficult to improve the accuracy of the detection model. To solve this problem, this paper proposed a deep-learning image steganalysis method based on generalized Gaussian distribution feature clustering. Firstly, a generalized Gaussian distribution is used to fit the coefficients of the image and extract statistical distribution features; Then, the extracted features are clustered to obtain corresponding category labels to achieve sample pre-classification; Finally, different subclasses of samples are used to train the network to achieve more reliable steganalysis. Experimental results show that this scheme can effectively divide images with similar statistical distributions into the same subcategory, reduce the impact of inconsistent statistical characteristics between the training image and the detection image on the steganalysis network, and improve the accuracy of steganalysis.