Image Forgery Detection System using VGG16 UNET Model
Open accessAbstract
The popularity of photography has grown as more individuals have access to camera equipment. Photos seem significant in our daily lives because they are rich with information. However, to learn more, images must often be enhanced. While many technologies are available to enhance the photographs, they are often used to produce phony shots, which leads to the spread of false information. The serious problem of image forgeries has to be addressed. To detect picture fraud, several well-established frameworks have been developed in the past. Recently, there has been an increase in the use of convolutional neural networks (CNNs), which has affected the localization of photo forgeries. One of the most difficult methods of picture falsification is image splicing, which involves cutting a section of one image into another. The picture forgery localization methods based on literature have a lot of drawbacks. As such, developing a technique for accurately and reliably identifying forgeries in photos that have been manipulated is crucial. We provide a potent deep learning-based approach that combines CNN with error level analysis (ELA) to detect picture fraud. To ascertain whether or not a picture has been manipulated, the ELA from an image is extracted and sent to CNN. Transfer learning with UNET (encoder-decoder model), and ELA is used to locate the tampered area. The recommended methods for predicting the changed location. The experiment results on the CASIA 2.0 database are quite encouraging, and these methodologies have been duly evaluated. The results of the recommended model outperform other well-known Transfer learning methodologies compared to the findings. The study suggests that the proposed method might get 0.917 and 0.935 accuracy and precision, respectively. Moreover, the graphical user interface—which enables us to determine the veracity of an image—is designed using these models.
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Fake-account activities can perform harmful illicit tasks including spoofs, phishing, or spamming, posing threats to users' privacy and safety.
“Fake-account activities can perform some harmful illicit tasks, including spoofs, phishing, or spamming, thereby potentially posing threats to the private and safe use of other users’ accounts in social networks.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [15]
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Encoder-decoder based convolutional neural networks for image forgery detection
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Image DeHazing Using Deep Learning Techniques
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Instagram fake profile detection using an ensemble learning method
Goyal, B., Gill, N. S., Gulia, P., Alduaiji, N., Shukla, P. K., & J, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-03973-x · Cited by 10 · see citing works
- Authors
- Choudhary, R. R., Paliwal, S., & Meena, G.
- Affiliations
- Central University of Rajasthan (IN)
- Corresponding author
- Meena, Gaurav
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2024
- Volume
- 235
- Pages
- 735-744
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4399198965
- Cited by
- 32
- References
- 19
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 2
- FWCI
- 6.63
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- 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
- image forgery detection, image splicing, tamper localization, error level analysis, VGG16, U-Net, transfer learning, image editing, image verification
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