Alam, Md. AshrafulAlam, Md. Golam RabiulIslam, NawshinAkash, Zahid MahbubDipro, Toriqul IslamMunna, Mehedi Hasan2025-09-302025-09-3020202020-10ID 16201003ID 16301146ID 16201106ID 16301195http://hdl.handle.net/10361/26810Cataloged from PDF version of thesis.Includes bibliographical references (pages 54-55).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.Deep fake images are content created by humans using the most advanced technologies. The technology imitates human expression to create fake images that are very similar to the real ones. Fake image creation has become very frequent due to rapid technological advancement. Again, with that advanced technology, it is possible to detect an image being fake or real. Therefore, we came up with the idea of giving a solution that which would be the most suitable network to rely on and why. Also which approach would be able to detect whether an image is real or fake most accurately. Using approaches of Neural Networks and Convolutional Neural Network, it becomes easier to decide and believe that which one's are real for sure. These deepfakes often creates misunderstandings among people. An image will be given to the system and it can verify whether this is a nely created fake image or not.63 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.CNN architecturesDeep learningFake imagesConvolutional neural networksGANNeural networks (Computer science).Image processing.Deepfakes--Identification.Thesis