Dual Convolutional Neural Network for Image Steganalysis In this paper, we propose a new steganalytic method that uses dual convolutional neural network (CNN) of which each has different inputs. To construct the dual CNN structure, two pairs of the preprocessing filters and the convolutional layers were brought from the conventional CNNbased steganalytic methods and the outputs of the dual CNN were concatenated and fed together into a following affine layer. Given an input image, a stego image is created by embedding some additional data into the input image using one of steganographic methods and a difference image is computed between the input and stego images. Then, the input and difference images are fed into each CNN, respectively. This indicates that the proposed method extracts learns additional features from the difference image using the additional CNN. Experimental results demonstrated that the proposed dual CNN with additional input can identify whether the SUNIWARD steganography was applied to the input image with an accuracy of 80.43%, and can improve the accuracy by approximately 5% when compared with the conventional CNNbased steganalytic method.

Depthwise Separable Convolutional Neural Network for Image Forensics Generalpurpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensicsrelated features from image patches with better parameter efficiency. When compared with four stateoftheart methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 xc3x97 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression.