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In this thesis, we present forgery image detection techniques for two of the most common image tampering techniques; copy-move and splicing. We used match points technique after feature extraction process using SIFT and SURF for detecting the copy-move forgery. For splicing detection, we extracted the edges if integral images of Y. Cb and Cr image components. Using Machine Learning techniques in classifying the images used to test our model that determines whether an image was fake or not, plus pointing out to the type of forgery used. We compared the results of detecting both the forgeries. We developed a GUI that detects both of the forgeries and gives detailed resultswhether a certain image was tampered or authenticated.Finally, we proposed an approach for image verification, using invisible watermark approach by embedding certaindata in an image.
About the author
Education: Bachelor of Computer Science, German University in Cairo, Egypt,May,2019 Publications: Y. William, S. Safwat and M. A. -. Salem, "Robust Image Forgery Detection Using Point Feature Analysis," 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), Leipzig, Germany, 2019, pp. 373-380.doi: 10.15439/2019F227
