Diffusion-based image inpainting forensics via weighted least squares filtering enhancement

The rapid development of blockchain technology has greatly changed people’s daily lives from financial market to healthcare field. Today, adulterated images appear in large numbers, causing a serious threat to personal privacy and social stability. At present, the research on blockchain is mainly focused on chain data transmission. Though the blockchain can ensure that the chain data transmission is not tampered, it is difficult to ensure that the data is real when placed on the system initially. In addition, the immutability will be destroyed when 51% attacks occur. Taking this point into consideration, it is necessary to identify the image authenticity on the blockchain. Diffusion-based inpainting is a common method of image tampering. Considering the blurring effect introduced by diffusion-based image inpainting, this paper proposes an image forensics method of diffusion-based image inpainting via weighted least squares filtering enhancement. The texture of the forged image is clear in the untouched regions, and the blurring effect leads to some texture changes in the inpainted regions. Weighted least squares filtering can preserve the texture structure of the untouched regions better and highlight the blurring effect of the inpainted regions. In view of the different reflects of the tampered information in different color channels, weighted least squares filtering is applied to enhance each color channel of the input image, which can capture the impact of image inpainting from multiple perspectives. The experimental results show that the proposed method not only makes up for the deficiency of previous blockchain forensics effectively, but also has better detection performance than the existing work.