[1]LIU Yaqi,CAI Qiang,SHI Lei,et al.Scalable constrained image splicing detection and localization with adversarial optimizing[J].CAAI Transactions on Intelligent Systems,2024,19(6):1479-1491.[doi:10.11992/tis.202307011]
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Scalable constrained image splicing detection and localization with adversarial optimizing

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1479-1491 Column: 学术论文—智能系统 Public date: 2024-12-05
Title:
Scalable constrained image splicing detection and localization with adversarial optimizing
Author(s):
LIU Yaqi1;  CAI Qiang2;  SHI Lei3;  ZHANG Yifan1;  LYU Binbin1;  XIA Chao1;  XU Shengwei1
1. Beijing Electronic Science and Technology Institute, Beijing 100070, China;
2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China;
3. State Key Laboratory of Media Conv
Keywords:
constrained image splicing detection and localization; scalable; correlation computation; adversarial learning; image forensics ; atrous convolution; pyramid pooling; depthwise separable convolution
CLC:
TP391.4
DOI:
10.11992/tis.202307011
Abstract:
A scalable detection and localization method, along with its adversarial optimization architecture, is proposed for the image forensics task of constrained image splicing detection and localization (CISDL). In the CISDL network, a novel scalable correlation computation module is constructed using high-efficiency channel attention enhancement blocks. The channel features are then calibrated using high-efficiency channel attention enhancement. Images of arbitrary sizes are processed using truncation operations on closely correlated factors, and a mask reconstruction network is designed based on depthwise separable convolution and residual connections. Finally, a patch-level adversarial learning strategy is proposed to optimize the pretrained model. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed method
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Last Update: 2024-11-05

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