On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection

Abstract

Detecting image resampling in re-compressed images is a very challenging problem. Existing approaches to image resampling detection operate by building pre-selected model to locate periodicities in linear predictor residues. Additionally, if an image was JPEG compressed before resampling, existing techniques detect tampering using the artifacts left by the pre-compression. However, state-of-the-art approaches cannot detect resampling in re-compressed images initially compressed with high quality factor. In this paper, we propose a novel deep learning approach to adaptively learn resampling detection features directly from data. To accomplish this, we use our recently proposed constrained convolutional layer. Through a set of experiments we evaluate the effectiveness of our proposed constrained convolutional neural network (CNN) to detect resampling in re-compressed images. The results of these experiments show that our constrained CNN can accurately detect resampling in re-compressed images in scenarios that previous approaches are unable to detect.