Image Forgery Localization based on Multi-Scale Convolutional Neural Networks-Reference-Cited by-同舟云学术

Image Forgery Localization based on Multi-Scale Convolutional Neural Networks

Published:2018-06-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security
language:
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Author:

Liu Yaqi¹,Guan Qingxiao¹,Zhao Xianfeng¹,Cao Yun¹

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

Funder

Fundamental Theory and Cutting Edge Technology Research Program of IIE CAS

National Key Technology R&D Program

NSFC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3206004.3206010

Reference21 articles.

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2. A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer

3. Yuri Boykov and Vladimir Kolmogorov . 2004. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE transactions on pattern analysis and machine intelligence Vol. 26 9 (2004) 1124--1137. 10.1109/TPAMI.2004.60 Yuri Boykov and Vladimir Kolmogorov . 2004. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE transactions on pattern analysis and machine intelligence Vol. 26 9 (2004) 1124--1137. 10.1109/TPAMI.2004.60

4. Median Filtering Forensics Based on Convolutional Neural Networks

5. Determining Image Origin and Integrity Using Sensor Noise

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