Compared with convolutional neural network (CNN), Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer vision. Due to the particularity of steganography, however, almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding artifacts. In this paper, we propose a novel convolutional Transformer network for color image steganalysis. Specifically, we firstly obtain various image residuals for each color channel of an input image in the pre-processing module. To capture more comprehensive steganalytic features, the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer group. The CNN group aims to extract local receptive fields features, while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic features. Finally, we employ a global covariance pooling (GCP) and two fully-connected (FC) layers with dropout for classification. Extensive comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art results. Although the proposed method is originally designed for color images, it can also obtain competitive results for grayscale images compared with the current best detector. In addition, we provide numerous ablation studies to verify the rationality of the proposed network architecture.

Abstract In this paper, a new class of image texture operators is proposed. We firstly determine that the number of gray levels in each $B\times B$ sub-block is a fundamental property of the local image texture. Thus, an occurrence histogram for each $B\times B$ sub-block can be utilized to describe the texture of the image. Moreover, using a new multi-bit plane strategy, i.e., representing the image texture with the occurrence histogram of the first one or more significant bit-planes of the input image, more powerful operators for describing the image texture can be obtained. The proposed approach is invariant to gray scale variations since the operators are, by definition, invariant under any monotonic transformation of the gray scale, and robust to rotation. They can also be used as supplementary operators to local binary patterns (LBP) to improve their capability to resist illuminance variation, surface transformations, etc.