Fast SwT-Based Deep Steganalysis Network for Arbitrary-Sized Images
Shaowei Weng, Shiyao Sun, Lifang Yu
Abstract
In this letter, a novel deep steganalysis network called SwT-SN is proposed by integrating directional difference adaptive combination (DDAC) followed by three residual blocks, convolutional spatial pyramid pooling equipped with size-independent detector (CSPP-SID) as well as a two-part of Swin transformer (SwT) structure suitable for steganalysis, aiming at enhancing the detection accuracy for arbitrary-sized images while significantly reducing training and test cost. In comparison to simply exploiting DDAC as preprocessing, DDAC + the residual structure can better improve the signal-to-noise ratio of the residual maps by suppressing the image content. In addition, CSPP-SID is innovatively proposed to convert feature maps of any size into feature vectors with fixed dimension, helping SwT-SN achieving high detection accuracy for arbitrary-sized images. Finally, a two-part structure of SwT with a fixed number of patches is firstly designed for image steganalysis to greatly reduce training cost by calculating the multi-head self-attention mechanism in the shifted window. Extensive experiments on two benckmark databases verify that SwT-SN has higher detection accuracy and shorter training cost compared to two prior state-of-the-art networks.