Hierarchical image resampling detection based on blind deconvolution

Resampling detection is a helpful tool in multimedia forensics; however, it is a challenge task in cases with compression and noisy. In this paper, by modeling the recovery of edited images using an inverse filtering process, we propose a novel resampling detection framework based on blind deconvolution. Different interpolation types in the resampling process can be distinguished by our algorithm, which is significant for practical forensics scenarios. Furthermore, in contrast to traditional resampling detection algorithms, our method can effectively avoid interference caused by JPEG block artifacts. As the experimental results show, our method is more robust than other state-of-the-art approaches in the case of strong JPEG compression and substantial Gaussian noise.