Forgery Localization Via Extracting Generic Features And Multiple Prior Fusion

Jiaqi Zhang; Liqiong Jian; Ziping Ma; Jinlin Ma; Xiaoshuai Huang

doi:10.64509/jicn.22.94

Authors

Jiaqi Zhang School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China Author
Liqiong Jian Blood Center of Ningxia Hui Autonomous Region, Yinchuan 750011, China Author
Ziping Ma School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China Author
Jinlin Ma School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China Author
Xiaoshuai Huang Beijing Advanced Center of Cellular Homeostasis and Aging-Related Diseases, Biomedical Engineering Department, International Cancer Institute, Institute of Advanced Clinical Medicine, Health Science Center, Peking University, Beijing 100191, China Author

DOI:

https://doi.org/10.64509/jicn.22.94

Keywords:

Image Forensics, Image Forgery Localization, Copy-Move, Splicing, Multi-Prior Fusion

Abstract

Nowadays, it is easy to create fake images using image editing software, which poses many security risks. Many current deep learning approaches tend to mix multiple types of faked samples for training to improve the model's generality. This ignores the unique nature of the different forged image types. In this paper, we provide a new perspective on training strategies for the generality of forensic tasks. Our analysis and experiments suggest that, under the conditions of this study, copy-move samples may be more effective in learning generic features than splicing samples. To explore this generic feature, we trained using only copy-move samples and tested both splicing and copy-move samples. In addition, we observed that original images of different quality underwent the same forgery process and did not produce precisely the same forgery cues, even though they had the same semantic information. Therefore, in order to improve the generalisation of the method to different datasets, three copy-move datasets were created based on the different qualities of the original images. Instead of mixing the three copy-move datasets directly for training, we use a multi-prior fusion strategy for training. The effectiveness of our proposed method was demonstrated through experimental testing on the public datasets.

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