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Cited by 33Digital Media Forensic DetectionAdvanced Steganography and Watermarking TechniquesChaos-based Image/Signal Encryption

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Unveiling Copy-Move Forgeries: Enhancing Detection With SuperPoint Keypoint Architecture

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Diwan, Anjali, Kumar, Dinesh, Mahadeva, Rajesh, Perera, H. C. S., Alawatugoda, Janaka·2023·IEEE Access
DOI 10.1109/access.2023.3304728
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Abstract

The authentication of digital images poses a significant challenge due to the wide range of image forgery techniques employed, with one notable example being a copy-move forgery. This form of forgery involves duplicating and relocating segments of an image within the same image, often accompanied by geometric transformations to deceive viewers into perceiving the forged image as authentic. Furthermore, additional processing techniques like scaling, rotation, JPEG compression, and the application of Additive White Gaussian Noise (AWGN) are frequently employed to further obscure any traces of forgery, making the detection and verification process even more complex. This paper presents a novel approach for detecting copy-move forgery in digital images using the self-supervised image keypoint detector, SuperPoint. Our approach leverages the advanced capabilities of SuperPoint, which combines keypoint detection and descriptor extraction, to identify and localize copy-move forgery accurately. One important aspect of our approach is its ability to handle images with different textures, including smooth and self-similar structural images. The proposed approach is able to produce stable results in images with various attacks, making it a functional and reliable tool for detecting copy-move forgery in a diverse range of forged images. Comparative analysis with existing forgery detection methods shows the superior performance of our proposed approach. Furthermore, the computational efficiency of our algorithm enables real-time forgery detection. Our approach using SuperPoint offers an effective solution for detecting copy-move forgery in digital images, making it valuable for image forensics and authenticity.

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Metadata

Authors
Diwan, A., Kumar, D., Mahadeva, R., Perera, H. C. S., & Alawatugoda, J.
Affiliations
  • Marwadi University (IN)
  • Indian Institute of Technology Roorkee (IN)
  • Khalifa University of Science and Technology (AE)
  • Griffith University (AU)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
11
Pages
86132-86148
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2023.3304728
OpenAlex
W4385819677
Cited by
33
References
1
Citations by year
2026: 11 · 2025: 11 · 2024: 10 · 2023: 1
FWCI
3.00
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Griffith Research Online (submittedVersion, cc-by-nc-nd)
  • Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, cc-by-nc-nd)
Funders
Rabdan Academy, Abu Dhabi, United Arab Emirates (UAE), funded by the Research Internal Fund
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
Keywords
copy-move forgery detection, SuperPoint, image forgery detection, image authentication, keypoint detection, descriptor extraction, self-supervised learning, geometric transformations, JPEG compression, additive white Gaussian noise, image forensics

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CoChat. (2026). Citation check: Unveiling Copy-Move Forgeries: Enhancing Detection With SuperPoint Keypoint Architecture. https://app.cochat.ai/papers/10.1109/access.2023.3304728
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Diwan, A., Kumar, D., Mahadeva, R., Perera, H. C. S., & Alawatugoda, J. (2023). Unveiling Copy-Move Forgeries: Enhancing Detection With SuperPoint Keypoint Architecture. IEEE Access. https://doi.org/10.1109/access.2023.3304728