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A Deep Learning Approach for Digital Image Forgery Detection Using Error Level Analysis and Convolutional Neural Networks
Authors
1 2 3 4 5 Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 127-130
- Article Type
- Research Article
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References
1. Zhang, Z., & Li, S. (2023). Deep learning for image forgery detection. Journal of Computer Vision, 14(2).
2. Smith, J., & Kumar, R. (2022). Machine learning applications in digital media forensics. IEEE Transactions on Multimedia, 22(5).
3. Wang, L., & Yang, X. (2021). Image splicing detection using convolutional neural networks. Proceedings of the IEEE International Conference on Computer Vision.
4. Li, J., & Zhang, H. (2022). An efficient approach to detecting copy-move forgeries in digital images. Journal of Digital Forensics, 16(1).
5. Lee, M., & Park, Y. (2022). Recent advancements in deepfake detection. IEEE Access, 10.
6. Wang, Y., & Zhao, Z. (2021). Forensic analysis of digital images with machine learning techniques. Springer Advances in Image Processing.
7. Gupta, R., & Verma, N. (2022). Image forgery detection and verification: Challenges and solutions. Computers, Materials & Continua, 70(3).
8. Zhou, B., & Yang, J. (2020). Error level analysis for digital image forensics. Journal of Image and Vision Computing, 56(4).
9. Chen, H., & Li, W. (2021). A review of image forgery detection techniques using machine learning. International Journal of Computer Applications, 9(3).
10. Choi, S., & Kim, J. (2021). Forgery detection using deep learning and Error Level Analysis. Proceedings of the IEEE International Conference on Image Processing.
11. Singh, A., & Verma, R . (2021). Analysis of copy-move forgery in digital images using CNNs. Computers & Security, 88.
12. Alper, M., & Emek, A. (2021). Detecting deepfake videos using convolutional neural networks. Journal of Digital Media and Communication, 11(2).
13. Kapoor, M., & Singh, H. (2022). Machine learning for image manipulation detection in social media. Social Media Analytics Journal, 15(6).
14. Yao, T., & Zhang, Y. (2022). A comparative study of deep learning models for digital image forgery detection. Neural Computing & Applications, 34(7).
15. Li, F., & Zhao, X. (2021). Deep learning methods for detecting deepfake videos. Computers in Human Behavior, 118.
16. Patel, D., & Rao, K. (2021). The use of CNNs for detecting image splicing in large datasets. Journal of Artificial Intelligence Research, 44.
2. Smith, J., & Kumar, R. (2022). Machine learning applications in digital media forensics. IEEE Transactions on Multimedia, 22(5).
3. Wang, L., & Yang, X. (2021). Image splicing detection using convolutional neural networks. Proceedings of the IEEE International Conference on Computer Vision.
4. Li, J., & Zhang, H. (2022). An efficient approach to detecting copy-move forgeries in digital images. Journal of Digital Forensics, 16(1).
5. Lee, M., & Park, Y. (2022). Recent advancements in deepfake detection. IEEE Access, 10.
6. Wang, Y., & Zhao, Z. (2021). Forensic analysis of digital images with machine learning techniques. Springer Advances in Image Processing.
7. Gupta, R., & Verma, N. (2022). Image forgery detection and verification: Challenges and solutions. Computers, Materials & Continua, 70(3).
8. Zhou, B., & Yang, J. (2020). Error level analysis for digital image forensics. Journal of Image and Vision Computing, 56(4).
9. Chen, H., & Li, W. (2021). A review of image forgery detection techniques using machine learning. International Journal of Computer Applications, 9(3).
10. Choi, S., & Kim, J. (2021). Forgery detection using deep learning and Error Level Analysis. Proceedings of the IEEE International Conference on Image Processing.
11. Singh, A., & Verma, R . (2021). Analysis of copy-move forgery in digital images using CNNs. Computers & Security, 88.
12. Alper, M., & Emek, A. (2021). Detecting deepfake videos using convolutional neural networks. Journal of Digital Media and Communication, 11(2).
13. Kapoor, M., & Singh, H. (2022). Machine learning for image manipulation detection in social media. Social Media Analytics Journal, 15(6).
14. Yao, T., & Zhang, Y. (2022). A comparative study of deep learning models for digital image forgery detection. Neural Computing & Applications, 34(7).
15. Li, F., & Zhao, X. (2021). Deep learning methods for detecting deepfake videos. Computers in Human Behavior, 118.
16. Patel, D., & Rao, K. (2021). The use of CNNs for detecting image splicing in large datasets. Journal of Artificial Intelligence Research, 44.
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