The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection
Open accessAbstract
Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model’s weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks’ outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model’s performance. The experimental results established the proposed model’s superiority over recently developed approaches.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 Krishnaraj et al., 2022
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Tampering Detection and Localization Through Clustering of Camera-Based CNN Features
Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E. J., & Tubaro, S. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.232 · Cited by 222
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2020) · IEEE Access · doi:10.1109/access.2020.3009877 · Cited by 140
Recent advances in digital image manipulation detection techniques: A brief review
Thakur, R., & Rohilla, R. (2020) · Forensic Science International · doi:10.1016/j.forsciint.2020.110311 · Cited by 132
Image Forgery Localization via Integrating Tampering Possibility Maps
Li, H., Luo, W., Qiu, X., & Huang, J. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2656823 · Cited by 115
Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics
Rodríguez-Ortega, Y., Ballesteros, D. M., & Renza, D. (2021) · Journal of Imaging · doi:10.3390/jimaging7030059 · Cited by 102
Abdalla, Y., Iqbal, M. T., & Shehata, M. (2019) · Information · doi:10.3390/info10090286 · Cited by 77
Gani, G., & Qadir, F. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102510 · Cited by 66
Robust forgery detection for compressed images using CNN supervision
Diallo, B. S., Urruty, T., Bourdon, P., & Fernández-Maloigne, C. (2020) · Forensic Science International Reports · doi:10.1016/j.fsir.2020.100112 · Cited by 65
A deep-learning-based image forgery detection framework for controlling the spread of misinformation
Ghai, A., Kumar, P., & Gupta, S. (2021) · Information Technology and People · doi:10.1108/itp-10-2020-0699 · Cited by 63
Multi-semantic CRF-based attention model for image forgery detection and localization
Rao, Y., Ni, J., & Xie, H. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108051 · Cited by 59
Tinnathi, S., & Sudhavani, G. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102966 · Cited by 45
Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization
Bi, X., Zhang, Z., & Xiao, B. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01403 · Cited by 40
Detection and localization of forgery using statistics of DCT and Fourier components
Dua, S., Singh, J., & Parthasarathy, H. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115778 · Cited by 40
A Robust Copy-Move Forgery Detection In Digital Image Forensics Using SURF
Badr, A., Youssif, A. A. A., & Wafi, M. (2020) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs49300.2020.9116433 · Cited by 32
DenseNet with Spatial Pyramid Pooling for Industrial Oil Palm Plantation Detection
Abdani, S. R., & Zulkifley, M. A. (2019) · International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE) · doi:10.1109/morse48060.2019.8998735 · Cited by 31
Image Forgery Detection and Localization via a Reliability Fusion Map
Yao, H., Xu, M., Qiao, T., Wu, Y., & Zheng, N. (2020) · Sensors · doi:10.3390/s20226668 · Cited by 29
Reliability Map Estimation for CNN-Based Camera Model Attribution
Güera, D., Zhu, F. M., Yarlagadda, S. K., Tubaro, S., Bestagini, P., & Delp, E. J. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00111 · Cited by 29
Hashmi, A. S., & Ahmad, T. (2019) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.09.007 · Cited by 23
Li, J., & Dong, P. (2019) · Global Energy Interconnection · doi:10.1016/j.gloei.2019.11.008 · Cited by 15
Dixit, A., & Bag, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107893 · Cited by 9
Research works printed but not linked: 3
- A. Doegar, 2019, Cnn based image forgery detection using pre-trained alexnet model, International Journal of Computational Intelligence & IoT, 2
- 10.5555/2969033.2969125 · DOI
- X. L. Li, 2002, Geothermal-solar combined organic rankine cycle power generation technology research, System Engineering Theory and Practice, 35, 32
Cited by
Machine 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 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
Chaitra, B., & Reddy, P. V. B. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110508 · Cited by 38
Maashi, M. S., Alamro, H., Mohsen, H., Negm, N., Mohammed, G. P., Ahmed, N. A., Ibrahim, S. S., & Alsaid, M. I. (2023) · IEEE Access · doi:10.1109/access.2023.3304237 · Cited by 34
Metadata
- Authors
- Krishnaraj, N., Sivakumar, B., Kuppusamy, R., Teekaraman, Y., & Thelkar, A. R.
- Affiliations
- SRM Institute of Science and Technology (IN)
- Sri Sairam College of Engineering (IN)
- University of Sheffield (GB)
- Jimma University (ET)
- Corresponding author
- Teekaraman, Yuvaraja
- Thelkar, Amruth Ramesh
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2022-01-31
- Volume
- 2022
- Pages
- 1-13
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- PMC
- PMC8820863
- OpenAlex
- W4210362228
- Cited by
- 37
- References
- 22
- Citations by year
- 2025: 10 · 2024: 9 · 2023: 9 · 2022: 9
- FWCI
- 3.03
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-10-04)
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 forgery detection, generative adversarial networks, dense convolutional network, extreme learning machine, artificial fish swarm algorithm, hyperparameter optimization, automated model design
- MeSH terms
- Deep Learning, Algorithms, Animals, Humans, Image Processing, Computer-Assisted
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
