Image Forgery Detection Using Machine Learning

• Published in: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) pp. 1 - 6
• Main Authors: Latha, K., Kavitha, D., Hemavathi, S., Velmurugan, K. Jayasakthi, R, Neathi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08-12-2022
• Subjects: Anti-Forensic Contrast-Enhanced Detector (AFCED) and Source Camera Identifier (SCI); Digital images; Forgery Detection; Machine learning; Machine learning algorithms; Social networking (online); Splicing; Support Vector Machine(SVM); Support vector machines; Tampered Area; Wearable computers
• DOI: 10.1109/ICPECTS56089.2022.10046422

Imaging technology has made tremendous progress in recent years, and digital images can now be captured by a variety of handheld devices such as mobile phones, wearables, tabs, and camcorders. These images are shared daily on social networks. Meanwhile, photo-editing software tools are making a comeback from the dawn of social networking, and filtering is just a few clicks away from your smart phone. These latest advances in image editing tools have eroded trust in digital images and cast doubt on their integrity and authenticity. Here, we use a machine learning algorithm (SVM) to effectively detect if an image has been manipulated and block users who try to upload altered images.