The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
An efficient approach for copy-move image forgery detection using convolution neural network
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“We noticed that all of these deep learning methods are designed to detect specific forms of tampering, such as copy-move, image-splicing.”
From Machine learning based medical image deepfake detection: A comparative study (2022) · cites this paper as Koul et al., 2022
Citations
Cites
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
Amerini, I., Ballan, L., Caldelli, R., Bimbo, A. D., & Serra, G. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2129512 · Cited by 1,068
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries
Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S., & Roy–Chowdhury, A. K. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2895466 · Cited by 493
Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching
Pun, C., Yuan, X., & Bi, X. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2423261 · Cited by 305
Pixel-Based Image Forgery Detection: A Review
Ansari, M. D., Ghrera, S. P., & Tyagi, V. (2014) · IETE Journal of Education · doi:10.1080/09747338.2014.921415 · Cited by 130
Mahmood, T., Mehmood, Z., Shah, M., & Saba, T. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.03.015 · Cited by 124
Copy-move and splicing image forgery detection and localization techniques: a review
Asghar, K., Habib, Z., & Hussain, M. (2016) · Australian Journal of Forensic Sciences · doi:10.1080/00450618.2016.1153711 · Cited by 113
A forgery detection algorithm for exemplar-based inpainting images using multi-region relation
Chang, I., Yu, J.-W., & Chang, C.-C. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.09.002 · Cited by 91
Agarwal, R., & Verma, O. P. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08495-z · Cited by 82
An efficient method for image forgery detection based on trigonometric transforms and deep learning
azrak, F. M. A., Sedik, A., Dessowky, M. I., El‐Banby, G. M., Khalaf, A. A. M., Elkorany, A. S., & El‐Samie, F. E. A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08162-3 · Cited by 60
Convolutional Neural Network for Copy-Move Forgery Detection
Abdalla, Y., Iqbal, M. T., & Shehata, M. (2019) · Symmetry · doi:10.3390/sym11101280 · Cited by 58
Research works printed but not linked: 4
- Kashyap RSPMAHGA (2017) An evaluation of digital image forgery detection approaches. Int J Appl Eng Res 17:4747–4758
- Paul KH, Akshatha KR, Karunakar AK, Seshadri S (2020) SURF based copy move forgery detection using kNN mapping. Proceed Comp Vision Conf:234–245 · DOI
- Qureshi MA, Deriche M (2014) A review on copy move image forgery detection techniques. Proceedings of the 11th International Multi-Conference on Systems, Signals & Devices (SSD14)Barcelona
- Yadav JA, Dongre N (2017) Analysis of copy-move forgery detection in digital images. Int J Eng Dev Res 5(1):732–736
Cited by
Sorted by citations
Mahajan, H. B., & Junnarkar, A. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15204-4 · Cited by 114
RetractedMachine learning based medical image deepfake detection: A comparative study
Solaiyappan, S., & Wen, Y. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100298 · Cited by 73
Enhancing Digital Image Forgery Detection Using Transfer Learning
Khalil, A. H., Ghalwash, A. Z., Abdel-Galil, H., Salama, G. I., & Ghalwash, H. A. (2023) · IEEE Access · doi:10.1109/access.2023.3307357 · Cited by 70
Image forgery techniques: a review
Kaur, G., Singh, N., & Kumar, M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10211-7 · Cited by 61
Self-citationImage forgery detection: comprehensive review of digital forensics approaches
Singh, S. P., & Kumar, R. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00265-8 · Cited by 58
Metadata
- Authors
- Koul, S., Kumar, M., Khurana, S. S., Mushtaq, F., & Kumar, K.
- Affiliations
- Central University of Punjab (IN)
- Maharaja Ranjit Singh Punjab Technical University (IN)
- Panjab University (IN)
- Corresponding author
- Kumar, Munish
- Venue
- Multimedia Tools and Applications
- Published
- 2022-02-17
- Volume
- 81
- Issue
- 8
- Pages
- 11259-11277
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W4212815050
- Cited by
- 55
- References
- 29
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 20 · 2023: 17 · 2022: 6
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multimedia Tools and Applications (publishedVersion)
- 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 › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Keywords
- copy-move forgery detection, image tampering, splicing, convolutional neural networks
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- CoChat. (2026). Citation check: An efficient approach for copy-move image forgery detection using convolution neural network. https://app.cochat.ai/papers/10.1007/s11042-022-11974-5
- The paper
- Koul, S., Kumar, M., Khurana, S. S., Mushtaq, F., & Kumar, K. (2022). An efficient approach for copy-move image forgery detection using convolution neural network. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-11974-5
