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Nivedha R., Ranjana S.
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- Student, Department of Computer Science, Anna Adarsh College for Women, Chennai, India, Assistant Professor, Department of Computer Science, Anna Adarsh College for Women, Chennai, Department of Computer Science, Anna Adarsh College for Women, Chennai, Tamil Nadu, Tamil Nadu, India, India
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Abstract
nIn today’s modern era, ensuring the authenticity of pictures is crucial. This article presents a straightforward method for identifying fake pictures using the widely used OpenCV and MD5 technologies. OpenCV helps us examine pictures for irregularities such as abnormal colors or shapes, while MD5 is a unique digital fingerprint for each image. Our approach combines these tools to create a robust fraud detection system. OpenCV carefully examines various aspects of pictures, flagging potential areas of manipulation. In addition, MD5 generates a unique code or hash that effectively represents the entire image. We tested our method on various datasets containing both genuine and fake pictures. The results demonstrate that our system accurately detects manipulated regions and verifies authenticity by comparing unique MD5 hashes. This research provides a practical solution for ensuring image integrity in law, medicine, and interactive media.
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Keywords: The topics covered are digital images, image forgery, authenticity, conventional techniques, OpenCV, detecting fraud, and MD5.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Operating Systems Development & Trends(joosdt)]
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References
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1. Appalanaidu P, Sanjana P, Jyothika S, Students T. IMAGE FORGERY DETECTION USING OPEN-CV AND MD5. 1998. Available from: http://www.journal-iiie-india.com/1_apr_23/30_online.pdf
2. Image Forgery Detection Using Machine Learning-Javatpoint. www.javatpoint.com. 2021. Available from: https://www.javatpoint.com/image-forgery-detection-using-machine-learning
3. Image Forgery Detection Using Machine Learning-Javatpoint. www.javatpoint.com. 2021. Available from: https://www.javatpoint.com/image-forgery-detection-using-machine-learning
4. Zheng, L.; Zhang, Y.; Thing, V.L. A survey on image tampering and its detection in real-world photos. J. Vis. Commun. Image Represent. 2019, 58, 380–399.Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 5–9 January 2021; pp. 375–384.
5. Sevinc Bayram, Husrev Taha Sencar, and Nasir Memon, “An efficient and robust method for detecting copy-move forgery,” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1053–1056, April 2009, Taipei, Taiwan.
6. Divya G. Python Image Forgery Detection using MD5 OpenCV. Ameerpet. 2023. Available from: https://www.ameerpet.org/python-image-forgery-detection-using-md5-opencv.html
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8. Laroudie C, Bursuc A, Ha ML, Franchi G. Improving CLIP Robustness with Knowledge Distillation and Self-Training. arXiv preprint arXiv:2309.10361. 2023 Sep 19.
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Journal of Operating Systems Development & Trends
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | April 9, 2024 | |
| Accepted | April 12, 2024 | |
| Published | May 3, 2024 |
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