重庆工商大学学报（自然科学版）2025，Vol.42Issue(3)：63-69,7.DOI:10.16055/j.issn.1672-058X.2025.0003.008
融合残差块和注意力机制的JPEG压缩反取证
JPEG Compression Anti-forensics Integrating Residual Blocks and Attention Mechanism
摘要
Abstract
Objective To address the lack of balance between the quality of generated images and anti-forensics performance in JPEG compression anti-forensics methods,an anti-forensics model RBAM-JAF combining multi-level residual blocks and channel attention mechanism was designed.This model aimed to improve the quality of generated images and achieve a better balance between anti-forensics performance and image quality.Methods A framework based on generative adversarial networks(GANs)was employed,including a generator and a discriminator.The generator incorporated multi-level residual blocks and channel attention mechanisms to enhance the model's generalization capability and improve the representation of image features.Additionally,a feature fusion module was introduced to fully utilize features from all convolutional layers,in order to enhance the quality of generated images.Results According to the experimental results,compared with four existing anti-forensic methods(M1,M2,M3,and M4),the proposed method showed significant improvements.At QF=25,the PSNR values increased by 8.52％,3.31％,1.52％,and 0.07％ respectively,and the SSIM values increased by 12.89％,2.46％,1.90％,and 0.55％respectively.At QF=50,the PSNR values increased by 10.22％,2.21％,0.88％,and 0.19％respectively,and the SSIM values increased by 9.71％,1.52％,0.64％,and 0.21％respectively.At QF=75,the PSNR values increased by 18.26％,3.56％,3.80％,and 2.96％respectively,and the SSIM values increased by 10.83％,1.58％,1.16％,and 0.52％respectively.Additionally,the AUC values of the four detectors for QF=25、50,and 75 were close to or below 0.5.Conclusion Experimental results demonstrate that method M5 improves the visual quality of generated images compared with existing methods while effectively deceiving forensic detectors,achieving a better balance between anti-forensic performance and the quality of the generated images.关键词
JPEG压缩/JPEG反取证/生成对抗网络/残差块/通道注意力机制Key words
JPEG compression/JPEG anti-forensics/generative adversarial networks/residual blocks/channel attention mechanism分类
计算机与自动化引用本文复制引用
唐贝贝,陈磊,李若宇..融合残差块和注意力机制的JPEG压缩反取证[J].重庆工商大学学报（自然科学版）,2025,42(3):63-69,7.基金项目
安徽省高校科研重点项目(2022AH051582) （2022AH051582）
认知智能全国重点实验室开放课题资助(COGOS-2023HE02) （COGOS-2023HE02）
淮南市50科技之星创新团队项目(623076). （623076）
