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PS-Net: A Learning Strategy for Accurately Exposing the Professional Photoshop Inpainting

  • Published:2023 Issue: Volume: Page:1-13
  • ISSN:2162-237X
  • Container-title:IEEE Transactions on Neural Networks and Learning Systems
  • language:
  • Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Zhang Yushu1ORCID,Fu Zhibin1,Qi Shuren1ORCID,Xue Mingfu1ORCID,Cao Xiaochun2ORCID,Xiang Yong3ORCID

Affiliation:

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

3. School of Information Technology, Deakin Blockchain Innovation Laboratory, Deakin University, Burwood, VIC, Australia

Funder

State Key Laboratory of Information Security

Basic Research Program of Jiangsu Province

National Natural Science Foundation of China

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Brain Magnetic Resonance Image Inpainting via Deep Edge Region-based Generative Adversarial Network;Journal of Electrical Engineering & Technology;2024-08-29

2. Deformable Dynamic Sampling and Dynamic Predictable Mask Mining for Image Inpainting;IEEE Transactions on Neural Networks and Learning Systems;2023
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