Image Harmonization Method Based on Deep Forgery Detection

In computer vision and augmented reality fields, it is an important and challenging task to fuse foreground objects into the background scene and achieve image harmonization. Most of the current mainstream harmonization methods adjust the appearance of the image foreground to make it compatible with the background visually, but the room for improving the harmonization effect is limited. In order to further improve the performance, this paper proposes an image harmonization authenticity identification network. Construct a GAN model with the results of the forgery detection network as the judgment indicator and the existing encoder-decoder harmonization network using the generative adversarial mechanism. The two networks compete with each other to achieve the result that the authenticity identification network cannot recognize the reconstruction result of the harmonization network as a synthetic image. The image difference module and image illumination module are added to the counterfeiting identification network, which effectively enhances its identification performance. Qualitative and quantitative experiments were conducted on the public benchmark dataset iHarmony4. The results demonstrate that the proposed method outperforms all comparative methods. In particular, compared to the best-performing comparative method, there were improvements of 0.24 and 0.16dB in the MSE and PSNR, and this method has achieved excellent performance in image harmonization tasks.