CNN-Transformer Based Generative Adversarial Network for Copy-Move Source/ Target Distinguishment-Reference-Cited by-同舟云学术

CNN-Transformer Based Generative Adversarial Network for Copy-Move Source/ Target Distinguishment

Author:

Zhang Yulan¹^ORCID,Zhu Guopu¹^ORCID,Wang Xing²,Luo Xiangyang³^ORCID,Zhou Yicong⁴^ORCID,Zhang Hongli²^ORCID,Wu Ligang⁵^ORCID

Affiliation:

1. School of Computer Science and Engineering, Huizhou University, Huizhou, China

2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

3. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China

4. Department of Computer and Information Science, University of Macau, Macau, China

5. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Zhongyuan Science and Technology Innovation Leading Talent Project

Science and Technology Development Fund, Macau

University of Macau

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

Reference51 articles.

2. An efficient scheme for detecting copy–move forged images by local binary patterns;li;J Inf Hiding Multimedia Signal Process,2013

3. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput -Assist Intervent,2015

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