CNN-based Approach for Robust Detection of Copy-Move Forgery in Images

Authors

  • Arivazhagan S Mepco Schlenk Engineering College, India
  • Newlin Shebiah Russel Mepco Schlenk Engineering College, India
  • Saranyaa M Mepco Schlenk Engineering College, India
  • Shanmuga Priya R Mepco Schlenk Engineering College, India

DOI:

https://doi.org/10.4114/intartif.vol27iss73pp80-91

Keywords:

Forgery detection, Copy-move forgery, Deep learning, Digital forensics, Image authenticity

Abstract

With the rise of high-quality forged images on social media and other platforms, there is a need for algorithms that can recognize the originality. Detecting copy-move forgery is essential for ensuring the authenticity and integrity of digital images, preventing fraud and deception, and upholding the law. Copy-move forgery is the act of duplicating and pasting a portion of an image to another location within the same image. To address these issues, we propose two deep learning approaches - one using a custom architecture and the other using transfer learning. We test our method against a number of benchmark datasets and demonstrate that, in terms of accuracy and robustness against various types of image distortions, it outperforms current state-of-the-art methods. Our proposed method has applications in digital forensics, copyright defence, and image authenticity.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2024-01-05

How to Cite

S, A., Russel, N. S., M, S., & R, S. P. (2024). CNN-based Approach for Robust Detection of Copy-Move Forgery in Images. Inteligencia Artificial, 27(73), 80–91. https://doi.org/10.4114/intartif.vol27iss73pp80-91