Feature Compensation Network Based on Non-Uniform Quantization of Channels for Digital Image Manipulation Forensics

Nowadays, with the popularity of image editing software and technology, it has become very easy to manipulate an image. Maybe it has serious consequences and adverse effects because of dishonest information. Digital Image Manipulation Forensics (DIMF) has become an important research direction in order to confirm the authenticity of images, detect the manipulations that images have undergone, and avoid the misuse of image editing. Currently, most DIMF target the detection of multiple image manipulations with fixed parameters. However, we consider to detect image manipulation in a more complex scenario where the parameters are chosen to be arbitrary. In this paper, we propose a Feature Compensation Network (FCNet) based on non-uniform quantization of channels. Briefly, it contains three important parts: (1) feature enhancement block, which extracts and enhances valid information from low-level features and eliminates semantic gaps between them and the high-level features. (2) sensitivity estimation block, which obtains the importance coefficients of each channel and guides the non-uniform quantization of low-level features. (3) adaptive average pooling, which keeps the resolution of low-level features and high-level features consistent and ensures that subsequent feature fusion properly. Through extensive experiments, we have demonstrated the effectiveness of the proposed method on detecting multiple image manipulations