Feature compensation network based on non-uniform quantization of channels for digital image global manipulation forensics

作者：

Highlights：

• We propose a feature compensation network based on the non-uniform quantization of channels, which reuses low-level features to enhance the ability of the network to extract manipulated traces.

• We design a feature enhancement module to extract manipulated traces in the low-level features and eliminate semantic gaps between the low-level features and the high-level features. At the same time, the importance of the features of different channels is estimated by using the sensitive property of the CNN.

摘要

•For global manipulation forensics, the spatial information of the low-level features can assist in extracting more manipulated traces, and the semantic information of the high-level features can help understand which manipulation the input image has undergone, and one cannot be without the other.•We propose a feature compensation network based on the non-uniform quantization of channels, which reuses low-level features to enhance the ability of the network to extract manipulated traces.•We design a feature enhancement module to extract manipulated traces in the low-level features and eliminate semantic gaps between the low-level features and the high-level features. At the same time, the importance of the features of different channels is estimated by using the sensitive property of the CNN.

论文关键词：Image manipulation detection,Convolutional neural network,Feature compensation,Importance coefficients of channel,Non-uniform quantization

论文评审过程：Received 18 January 2022, Revised 25 April 2022, Accepted 13 June 2022, Available online 18 June 2022, Version of Record 2 July 2022.

论文官网地址：https://doi.org/10.1016/j.image.2022.116795