Recently, many steganalysis approaches improve their feature extraction ability through addingconvolutional layers. However, it often leads to a decrease of resolution in the feature map during downsampling,which make...Recently, many steganalysis approaches improve their feature extraction ability through addingconvolutional layers. However, it often leads to a decrease of resolution in the feature map during downsampling,which makes it challenging to extract weak steganographic signals accurately. To address this issue, this paperproposes a multi-resolution steganalysis net (MRS-Net). MRS-Net adopts a multi-resolution network to extract globalimage information, fusing the output feature map to ensure high-dimensional semantic information andsupplementing low-level detail information. Furthermore, the model incorporates an attention module which cananalyze image sensitivity based on different channel and spatial information, thus effectively focusing on areas withrich steganographic signals. Multiple benchmark experiments on the BOSSBase 1.01 dataset demonstrate that theaccuracy of MRS-Net significantly improves by 9.9% and 3.3% compared with YeNet and SRNet, respectively,demonstrating its exceptional steganalysis capability.展开更多
基金supported in part by the China Guangxi Science and Technology Plan Project(Guangxi Science and Technology Base and Talent Special Project)(No.2022AC20001)Chile CONICYT FONDECYT Regular Project(No.1181809)the Chile CONICYT FONDEF Program(No.ID16I10466).
文摘Recently, many steganalysis approaches improve their feature extraction ability through addingconvolutional layers. However, it often leads to a decrease of resolution in the feature map during downsampling,which makes it challenging to extract weak steganographic signals accurately. To address this issue, this paperproposes a multi-resolution steganalysis net (MRS-Net). MRS-Net adopts a multi-resolution network to extract globalimage information, fusing the output feature map to ensure high-dimensional semantic information andsupplementing low-level detail information. Furthermore, the model incorporates an attention module which cananalyze image sensitivity based on different channel and spatial information, thus effectively focusing on areas withrich steganographic signals. Multiple benchmark experiments on the BOSSBase 1.01 dataset demonstrate that theaccuracy of MRS-Net significantly improves by 9.9% and 3.3% compared with YeNet and SRNet, respectively,demonstrating its exceptional steganalysis capability.
