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A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

  • Published:2020 Issue: Volume:8 Page:133488-133502
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Marra FrancescoORCID,Gragnaniello DiegoORCID,Verdoliva LuisaORCID,Poggi GiovanniORCID

Funder

Google Faculty Research Award

Air Force Research Laboratory

Defense Advanced Research Projects Agency

PREMIER project

Italian Ministry of Education, University, and Research within the Progetti di Ricerca di Interesse Nazionale (PRIN) 2017 Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 70 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. D-Net: A dual-encoder network for image splicing forgery detection and localization;Pattern Recognition;2024-11

2. An efficient digital image forgery detection using Pelican search optimisation-based DCNN;Journal of Experimental & Theoretical Artificial Intelligence;2024-07-28

3. A systematic literature review on image splicing detection and localization using emerging technologies;Multimedia Tools and Applications;2024-07-23

4. Detecting Fake Images Using Neuro-Fuzzy Inference Systems: A Brief Comparative Analysis;2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE);2024-06-30

5. Image Forgery Detection using Convolutional Neural Networks and Blockchain Technology;Proceedings of the Cognitive Models and Artificial Intelligence Conference;2024-05-25
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