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.