Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction-Reference-Cited by-同舟云学术

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Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

Published:2024 Issue: Volume:12 Page:121675-121687
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shinde Varun¹^ORCID,Dhanawat Vineet²^ORCID,Almogren Ahmad³^ORCID,Biswas Anjanava⁴^ORCID,Bilal Muhammad⁵,Ali Naqvi Rizwan⁶^ORCID,Rehman Ateeq Ur⁷^ORCID

Affiliation:

1. Cloudera Inc., Austin, TX, USA

2. Meta Platforms Inc., Menlo Park, CA, USA

3. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

4. AWS AI & Machine Learning, San Diego, CA, USA

5. Department of Computer Engineering, HITEC University, Taxila, Pakistan

6. Department of AI and Robotics, Sejong University, Seoul, Republic of Korea

7. School of Computing, Gachon University, Seongnam, Republic of Korea

Funder

King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project number

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10659860.pdf?arnumber=10659860

Reference41 articles.

1. Extended Self in a Digital World: Table 1.

2. An Evaluation of Popular Copy-Move Forgery Detection Approaches

3. Thinking beyond the block: block matching for copy-move forgery detection revisited

4. Robust dense‐field based copy‐move forgery localization using generic radial harmonic Fourier moment invariants

5. An Efficient CNN Model to Detect Copy-Move Image Forgery

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