RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

Detecting a splicing forgery image and then locating the forgery regions is a challenging task. Some traditional feature extraction methods and convolutional neural network (CNN)-based detection methods have been proposed to finish this task by exploring the differences of image attributes between the un-tampered and tampered regions in an image. However, the performance of the existing detection methods is unsatisfactory. In this paper, we propose a ringed residual U-Net (RRU-Net) for image splicing forgery detection. The proposed RRU-Net is an end-to-end image essence attribute segmentation network, which is independent of human visual system, it can accomplish the forgery detection without any preprocessing and post-processing. The core idea of the RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN. The residual propagation recalls the input feature information to solve the gradient degradation problem in the deeper network; the residual feedback consolidates the input feature information to make the differences of image attributes between the un-tampered and tampered regions be more obvious. Experimental results show that the proposed detection method can achieve a promising result compared with the state-of-the-art splicing forgery detection methods.