Efficient approach towards detection and identification of copy move and image splicing forgeries using mask R-CNN with MobileNet V1
Open accessAbstract
With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.
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All of these deep learning methods are designed to detect specific forms of tampering, such as copy-move and image-splicing.
“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 Kadam et al., 2022
The Abstract describes the paper’s deep-learning model as detecting copy-move and image-splicing forgeries, and Related Work §2.2 describes a deep-learning method for image splicing. But the Introduction distinguishes forgery type-dependent from type-independent detection; the paper does not support the claim that all deep-learning methods target specific forms of tampering. Page numbers were not provided in our copy. (LLM, conf=0.84)
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- Authors
- Kadam, K. D., Ahirrao, S., & Kotecha, K.
- Affiliations
- Symbiosis International University (IN)
- Corresponding author
- Ahirrao, Swati
- Kotecha, Ketan
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2022-01-05
- Volume
- 2022
- Pages
- 1-21
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- PMC
- PMC8754624
- OpenAlex
- W4205170222
- Cited by
- 59
- References
- 67
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 14 · 2023: 22 · 2022: 6
- FWCI
- 4.42
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-12-13)
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Research Support Fund of Symbiosis International
- 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 splicing detection, image tampering, Mask R-CNN, MobileNetV1, lightweight deep learning
- MeSH terms
- Humans, Neural Networks, Computer
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