IMAGE STEGANALYSIS WITH CONVOLUTIONAL VISION TRANSFORMER

Recent research has shown that deep learning based methods offer more accurate detection for image steganalysis than the traditional detection paradigm based on rich media models. Existing network architectures based on deep learning, however, stack more and more convolutional layers to increase local receptive fields for image stegananlysis. Limited by hardware, the detector with several convolutional layers may not extract features of steganography images from a global perspective effectively. In this paper, we propose a Convolutional Vision Transformer for image stegananlysis, which can capture both local and global dependencies among noise features. In image processing phase, our network preserves CNN frame for its capacity of producing image noise residuals. Different from previous methods, we utilize the attention mechanism of vision transformer for feature extraction and classification. The proposed network is validated on two public image datasets (BOSSbase 1.01 and ALASKA #2). Experimental results demonstrate that our network performs well over fixed-size dataset and arbitrary-size dataset.