Image Splicing Detection via Camera Response Function Analysis

Can Chen, Scott McCloskey, Jingyi Yu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5087-5096

Recent advances on image manipulation techniques have made image forgery detection increasingly more challenging. An important component in such tools is to fake motion and/or defocus blurs through boundary splicing and copy-move operators, to emulate wide aperture and slow shutter effects. In this paper, we present a new technique based on the analysis of the camera response functions (CRF) for efficient and robust splicing and copy-move forgery detection and localization. We first analyze how non-linear CRFs affect edges in terms of the intensity-gradient bivariable histograms. We show distinguishable shape differences on real vs. forged blurs near edges after a splicing operation. Based on our analysis, we introduce a deep-learning framework to detect and localize forged edges. In particular, we show the problem can be transformed to a handwriting recognition problem an resolved by using a convolutional neural network. We generate a large dataset of forged images produced by splicing followed by retouching and comprehensive experiments show our proposed method outperforms the state-of-the-art techniques in accuracy and robustness.