Noise and Edge Based Dual Branch Image Manipulation Detection

Abstract

Unlike ordinary computer vision tasks that focus more on the semantic content of images, the image manipulation detection task pays more attention to the subtle information of image manipulation. In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subtle traces of manipulation. Meanwhile, the dual branch network, consisting of a high-resolution branch and a context branch, is used to capture the traces of artifacts. In general, most manipulation leaves artifacts on the manipulation region boundary. A specially designed manipulation edge detection module is constructed based on the dual branch network to identify these artifacts. We add a distance factor to the self-attention module to better describe the correlation between pixels. Experimental results on publicly available image manipulation datasets demonstrate the effectiveness of our model.