DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network

To solve the problem that for image steganalysis based on convolution neural network, manual designed filter kernels were used to extract residual characteristics, but in practice, these kernels filter were not suitable for each steganography algorithm and have worse performance in application, a directional difference adaptive combination (DDAC) method was proposed. Firstly, the difference was calculated between center pixel and each directional pixel around, and 1×1 convolution was adopted to achieve linear combinations of directional difference. Since the combination parameters self-adaptively update according to loss function, filter kernels could be more effective in extracting diverse residual characteristics of embedding information. Secondly, truncated linear unit (TLU) was applied to raise the ratio of embedding information residual to image information residual. The model's coveragence was accelerated and the ability of feature extraction was promoted. Experimental results indicate that substituting the proposed method could improve the accuracy of Ye-net and Yedroudj-net by 1.30%~8.21% in WOW and S-UNIWARD datasets. Compared with fix and adjustable SRM filter kernels methods, the accuracy of test model using DDAC increases 0.60%~20.72% in various datasets, and the training progress was more stable. DDAC-net was proved to be more effective in comparsion with other steganalysis model. © 2022, Editorial Board of Journal on Communications. All right reserved.