| Presenter Name: DR Mohamed Elaskily |
| Lecture Date:11/22/2021 11:00:00 AM |
| lecture Topic: PHD Thesis |
| Department: Informatics |
| Lecture Title: Enhancement of Forensic Methods for Digital Images |
| Lecture Summary: Digital images and their applications gained a huge interest in several fields. Image forgeries are applied to give the digital images other meanings or to deceive the viewers. Image forgeries appear in many cases such as cybercrimes, military and intelligence deception, electronic signature forgery, the evidences in courts, electronic documents modifications, social media, or defamation of important characters.The lecture covers different directions of Copy-Move Forgery Detection (CMFD) and gives a wide coverage of earlier CMFD algorithms and techniques. It also presents an approach to enhance the efficiency of using SIFT algorithm in detecting copy move forgery by two ways.Another innovative CMFD technique for automatic detection of copy-move forgery based on deep learning approaches is proposed. A Convolutional Neural Network (CNN) is specifically designed for CMFD application. The CNN is exploited to learn hierarchical feature representations from input images, which are used for detecting the forged and original images. |
| Presenter Profile: Click here |
| Lecture Slides: Download |
Lecture Link:
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| keywords: #Digital_image_forgery #Image_authentication #Copy_move_forgery #Image_splicing #Image_morphing #SIFT #SURF #morphological_operation #Object_detection #CCL #Deep_learning #CNN |
