Feature Extraction and Analysis of SIFT features for ELA of Authentic and Forged Images
Keywords:
Copy-move, Error Level Analysis (ELA), Image forgery, Scale Invariant Feature Transform (SIFT), Spliced imagesAbstract
With the advancements of technology in current era, everyone faces a challenge to identify digitally manipulated images. It is not easy to discriminate the original and forged images. For digital image tampering, image splicing and copy-move forgeries are very much well-known and common techniques. Image forgery is detected and spotted based on feature descriptor of an image. It is a concise and important local descriptor which is to be applied to grasp hierarchical representations from the input images. The significant correlation among nearby pixels has been identified by deep learning-based methods. It prefers locally grouped networks rather than one-to-one networks among all pixels. In the conducted research, the primary objective was to discern the authenticity of images using an integrated approach involving Error Level Analysis (ELA), Scale-Invariant Feature Transform (SIFT) features, and specific considerations for image types. These are Authentic, Copy-Move, and Spliced. The images under investigation were exclusively of JPEG format with size of 384x256 pixels. The Average SIFT feature values for the Authentic images consistently surpassed those of both Copy-Move and Spliced images. This discrepancy in feature values across the three categories, considering the standardized image format, presented a distinct opportunity for classification.
Downloads
References
Dong, J., Wang, W., & Tan, T. (2013, July). Casia image tampering detection evaluation database. In 2013 IEEE China summit and international conference on signal and information processing (pp. 422-426). IEEE.
Sudiatmika, I. B. K., Rahman, F., Trisno, T., & Suyoto, S. (2019). Image forgery detection using error level analysis and deep learning. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(2), 653-659.
Liu, Y. H. (2018, September). Feature extraction and image recognition with convolutional neural networks. In Journal of Physics: Conference Series (Vol. 1087, p. 062032). IOP Publishing.
Mahale, V. H., Ali, M. M., Yannawar, P. L., & Gaikwad, A. T. (2017). Image inconsistency detection using local binary pattern (LBP). Procedia computer science, 115, 501-508.
Mareen, H., Vanden Bussche, D., Guillaro, F., Cozzolino, D., Van Wallendael, G., Lambert, P., & Verdoliva, L. (2022, August). Comprint: Image forgery detection and localization using compression fingerprints. In International Conference on Pattern Recognition (pp. 281-299). Cham: Springer Nature Switzerland.
Chakraborty, S., Chatterjee, K., & Dey, P. (2022). Detection of Image Tampering Using Deep Learning, Error Levels & Noise Residuals..
Rhee, K. H. (2021). Generation of novelty ground truth image using image classification and semantic segmentation for copy-move forgery detection. IEEE Access, 10, 2783-2796.
Azhan, N. A. N., Ikuesan, R. A., Razak, S. A., & Kebande, V. R. (2022). Error Level Analysis Technique for Identifying JPEG Block Unique Signature for Digital Forensic Analysis. Electronics, 11(9), 1468.
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60, 91-110.
Alberry, H. A., Hegazy, A. A., & Salama, G. I. (2018). A fast SIFT based method for copy move forgery detection. Future Computing and Informatics Journal, 3(2), 159-165.
Liu, X., Liu, Y., Chen, J., & Liu, X. (2022). PSCC-Net: Progressive spatio-channel correlation network for image manipulation detection and localization. IEEE Transactions on Circuits and Systems for Video Technology, 32(11), 7505-7517.
Ali, S. S., Ganapathi, I. I., Vu, N. S., Ali, S. D., Saxena, N., & Werghi, N. (2022). Image Forgery Detection Using Deeplearning by Recompressing Images. Electronics 2022, 11, 403.
Kwon, M. J., Yu, I. J., Nam, S. H., & Lee, H. K. (2021). CAT-Net: Compression artifact tracing network for detection and localization of image splicing. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 375-384).
Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S., & Roy-Chowdhury, A. K. (2019). Hybrid lstm and encoder–decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing, 28(7), 3286-3300.
Yancey, R. E. (2019). Deep localization of mixed image tampering techniques. arXiv preprint arXiv:1904.08484.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
