Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 4024-4033.doi: 10.13229/j.cnki.jdxbgxb.20240437

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Spatial image steganography based on preprocessing layer enhancement and attention mechanism

Wei-wei LUO(),Chang-long LIU,Qin LEI()   

  1. School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
  • Received:2024-04-23 Online:2025-12-01 Published:2026-02-03
  • Contact: Qin LEI E-mail:luoweiwei@lzjtu.edu.cn;leiqin@mail.lzjtu.cn

Abstract:

In order to capture the changes of statistical features of images caused by stegography more comprehensively and improve the detection accuracy of spatial steganalysis, the embedding characteristics of the steganographic algorithm are incorporated. The derivative and Gabor double filter is used to preprocess the image, and the filter extraction is enhanced to produce a variety of residual images, which effectively increases the diversity of steganographic features. The optimized CBAM module is embedded into the residual block to guide the network to effectively focus on the region with rich steganographic signals, thus strengthening the discriminant learning ability and training effect of the network. The proposed model is compared with the classical model on BOSSbase v1.01 and BOWS2, and the experimental results show that the detection accuracy of the proposed method is superior to the existing mainstream models of Ye-Net, SRNet and ZhuNet.

Key words: steganographic analysis, convolutional neural network, pretreatment layer enhancement, attention module

CLC Number: 

  • TP309

Fig.1

Overall network structure"

Table 1

Filter details"

滤波器类型基本权重旋转角度/(°滤波器数量/个
类别1D1,0=0?????0???01-1???00?????0???0902
类别2D1,1=????1??-1-1???????1?1
类别3D2,0=0?????0????01-2????10?????0????0902
类别4D2,1=-1??????2??-1????1-2???????1????0??????0???????0902
类别5D3,0=0??????0???0??????0????0?0??????0???0??????0????01-3???3?-1????00??????0???0??????0????00??????0???0??????0????0902
类别6D4,0=0??????0?????0??????0????0?0??????0?????0??????0????01-4?????6-4????10??????0?????0??????0????00??????0?????0??????0????0902
类别7D4,2=?????1?-4????????6?-4??????1-2??????8-12??????8?-2????1?-4?????????6?-4??????1????0??????0?????????0??????0??????0????0??????0?????????0??????0??????0902
类别8D2,2=?????1?-2??????1-2???????4?-2?????1??-2?????11
类别9D3,3=????1?-3?????3??-1?-3?????9?-9???????3????3?-9??????9?-3-1?????3?-3???????1?1
类别10D4,4=????1???-4????????6????-4???????1-4??????16?-24??????16-4????6-24??????36??-24??????6-4??????16-24??????16??-4????1????-4????????6????-4??????11

Table 2

Comparison of false detection rates of 0.2 bpp WOW and S-UNIWARD algorithms for networks with and without mean fusion features. (Training and testing were carried out on the BOSS dataset)"

算法OriginalOriginal + Mean
WOW0.175 30.170 0
S-UNIWARD0.214 20.206 5

Fig.2

IECA module structure"

Fig.3

Structure of spatial attention"

Fig.4

CBAM+ structure diagram"

Table 3

Combined with network performance comparisons of different attention modules(Training and testing were conducted on the BOSS dataset)"

模型类型S-UNIWARDWOW
0.2 bpp0.4 bpp0.2 bpp0.4 bpp
未加注意力机制的模型0.256 50.137 20.199 50.114 0
添加CBAM的模型0.207 80.120 80.170 80.099 7
添加CBAM+的模型0.206 50.111 20.170 00.104 5

Table 4

Comparison of steganographic false detection rates under different algorithms(All networks were trained and tested on the BOSS dataset)"

网络模型S-UNIWARDWOWHUGO
0.1 bpp0.2 bpp0.3 bpp0.4 bpp0.1 bpp0.2 bpp0.3 bpp0.4 bpp0.1 bpp0.2 bpp0.3 bpp0.4 bpp

Ye-Net

SRNet

ZhuNet

本文算法

0.446 9

0.397 5

0.366 2

0.293 8

0.358 7

0.298 2

0.258 0

0.206 5

0.286 1

0.215 3

0.185 7

0.141 2

0.237 4

0.183 2

0.152 6

0.111 2

0.393 7

0.326 1

0.315 3

0.263 8

0.291 6

0.247 9

0.221 8

0.170 0

0.245 9

0.172 4

0.156 3

0.125 3

0.213 6

0.145 9

0.121 7

0.104 5

0.427 6

0.389 5

0.377 0

0.293 0

0.347 9

0.287 2

0.264 1

0.205 8

0.284 9

0.224 8

0.206 0

0.164 7

0.256 2

0.196 7

0.166 4

0.142 0

Fig.5

Comparison of steganographic false detection rates of different payloads on S-UNIWARD, WOW and HUGO using Ye-Net, SRNet, ZhuNet and the proposed algorithm"

Table 5

Comparison of steganographic error detection rates of different algorithms under BOWS2 data set enhancement"

算法BOSSBOSS + BOWS2
0.2 bpp 0.4 bpp0.2 bpp 0.4 bpp
Ye-Net0.291 6 0.213 60.256 9 0.142 9
SRNet0.247 9 0.145 90.192 2 0.093 5
ZhuNet0.221 8 0.121 70.173 5 0.087 4
本文算法0.170 0 0.104 50.147 0 0.068 3
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