  Image forgery detection is the task of detecting and localizing forged parts
in tampered images. Previous works mostly focus on high resolution images using
traces of resampling features, demosaicing features or sharpness of edges.
However, a good detection method should also be applicable to low resolution
images because compressed or resized images are common these days. To this end,
we propose a Shallow Convolutional Neural Network(SCNN), capable of
distinguishing the boundaries of forged regions from original edges in low
resolution images. SCNN is designed to utilize the information of chroma and
saturation. Based on SCNN, two approaches that are named Sliding Windows
Detection (SWD) and Fast SCNN, respectively, are developed to detect and
localize image forgery region. In this paper, we substantiate that Fast SCNN
can detect drastic change of chroma and saturation. In image forgery detection
experiments Our model is evaluated on the CASIA 2.0 dataset. The results show
that Fast SCNN performs well on low resolution images and achieves significant
improvements over the state-of-the-art.
