A Skip Connection Architecture for Localization of Image Manipulations

Detection and localization of image manipulations are becoming of increasing interest to researchers in recent years due to the significant rise of malicious content-changing image tampering on the web. One of the major challenges for an image manipulation detection method is to discriminate between the tampered regions and other regions in an image. We observe that most of the manipulated images leave some traces near boundaries of manipulated regions including blurred edges. In order to exploit these traces in localizing the tampered regions, we propose an encoder-decoder based network where we fuse representations from early layers in the encoder (which are richer in low-level spatial cues, like edges) by skip pooling with representations of the last layer of the decoder and use for manipulation detection. In addition, we utilize resampling features extracted from patches of images by feeding them to LSTM cells to capture the transition between manipulated and non manipulated blocks in the frequency domain and combine the output of the LSTM with our encoder. The overall framework is capable of detecting different types of image manipulations simultaneously including copy-move, removal, and splicing. Experimental results on two standard benchmark datasets (CASIA 1.0 and NIST’16) demonstrate that the proposed method can achieve a significantly better performance than the state-of-the-art methods and baselines.