Lightweight Steganography Detection Method Based on Multiple Residual Structures and Transformer

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外文摘要：Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features,yielding higher detection efficiency compared to manually designed stegano-graphy detection methods.Detection methods based on convolutional neural network frameworks can extract global features by increasing the network's depth and width.These frameworks are not highly sensitive to global features and can lead to significant resource consumption.This manuscript proposes a lightweight steganography detection method based on multiple residual structures and Transformer(ResFormer).A multi-residuals block based on channel rearrangement is designed in the preprocessing layer.Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability.A lightweight convolutional and Transformer feature extraction backbone is constructed,which reduces the computational and parameter complexity of the net-work by employing depth-wise separable convolutions.This backbone integrates local and global image features through the fusion of convolutional layers and Transformer,enhancing the network's ability to learn global features and effectively enriching feature diversity.An effective weighted loss function is introduced for learning both local and global features,BiasLoss loss function is used to give full play to the role of feature diversity in classification,and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features.Based on BossBase-1.01,BOWS2 and ALASKA#2,extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms,employing both classical and state-of-the-art steganalysis techniques.The experimental results demonstrate that compared to the SRM,SRNet,SiaStegNet,CSANet,LWENet,and SiaIRNet methods,the proposed ResFormer method achieves the highest reduction in the parameter,up to 91.82％.It achieves the highest improvement in detection accuracy,up to 5.10％.Compared to the SRNet and EWNet methods,the proposed ResFormer method achieves an improvement in detection accuracy for the J-UNIWARD algorithm by 5.78％and 6.24％,respectively.

外文关键词：

SteganalysisMultiple residual blocksTransformerChannel shuffle

作者：

Hao LI、Yi ZHANG、Jinwei WANG、Weiming ZHANG、Xiangyang LUO

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作者单位：

Key Laboratory of Cyberspace Situation Awareness of Henan Province,Zhengzhou 450001,China

Nanjing University of Information Science&Technology,Nanjing 210044,China

University of Science and Technology of China,Hefei 230052,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaZhongyuan Science and Technology Innovation Leading Talent Project,ChinaKey Research and Development Project of Henan Province

项目编号：

6217243562202495U23362062022YFB3102900214200510019221111321200

出版年：

2024

DOI：

10.23919/cje.2022.00.452

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(4)