抗深度取证的多粒度融合图像修复网络

doi:10.12066/j.issn.1007-2861.2456

Abstract

Abstract:

Considering that it is relatively easy to use technological tools to attack and tamper with digital media, image forensics technology has been studied extensively in the field of image security. In addition to developing more realistic image forgery operations, research on image forgery anti-forensics technology also promotes forensics technology development in the opposite direction. The process of image inpainting has always been a research hotspot. This paper proposes a multi-granularity fusion-based image inpainting network resistant to deep forensics (MGFR) network. MGFR network comprises three parts: a codec, multi-granularity generation module, and a multi-granularity attention module. First, the encoder encodes the input damaged image into depth features, and then the depth features are generated by the multi-granularity generation module into three intermediate features. Subsequently, we use the multi-granularity attention module to fuse the intermediate features of different granularities. As a final step, the fused features are passed through the decoder to produce the output. Additionally, the proposed MGFR is jointly supervised by reconstruction loss, pattern noise loss, deep forensic loss, and adversarial loss. Experimental results reveal that the proposed MGFR avoids the forensics of deep forensics networks while maintaining decent inpainting performance.

Key words: multimedia security, anti-forensics, image inpainting, multi-granularity

CLC Number:

TP391

DOU Liyun, FENG Guorui, QIAN Zhenxing, ZHANG Xinpeng. Multi-granularity fusion-based image inpainting network resistant to deep forensics[J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 10-23.

Figures/Tables 9

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References 52

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Multi-granularity fusion-based image inpainting network resistant to deep forensics

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