Hossain, Muhammad IqbalEqra, Zarin SyaraSaha, Neloy KumerKarim, Tayeba RounakHoque, TafsirulTabassum, Faria Naz2025-09-112025-09-1120252025-08ID 23101552ID 21201556ID 23141029ID 21201517ID 21201815http://hdl.handle.net/10361/26706Cataloged from PDF version of thesis.Includes bibliographical references (pages 58-64).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Deepfake technology has advanced rapidly, producing highly realistic synthetic videos that can evade state-of-the-art detectors. However, most models trained on curated benchmarks fail to generalize to unseen datasets. This work examines the causes of such failures and explores solutions, introducing VeriFake, a custom dataset of 500 real and 500 fake videos generated using Roop and FaceFusion to reflect modern high-quality manipulations. We trained CNN and Transformer-based models on various combinations of VeriFake, Celeb-DF, and FaceForensics++ (FF++), with and without domain adaptation techniques such as Gradient Reversal Layers (GRL) and heuristic features. While VeriFake-trained models excelled in-domain, they generalized poorly to DFDC and Celeb-DF due to reliance on dataset-specific artifacts. To address this, we developed a multi-branch Xception-based framework with disentangled feature learning, a reconstruction decoder, and a GRL-powered domainadversarial module and trained on FF++, achieving 0.7509 AUC on DFDC and 0.8183 AUC on Celeb-DF, surpassing results from existing works, though with a trade-off on VeriFake (0.7054 AUC). These findings highlight the importance of diverse, well-designed benchmarks and domain-invariant features for robust real-world deepfake detection.64 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.Deepfake detectionGeneralizationCross-dataset performanceVerifake datasetFaceFusionNeural networksTransformersContrastive learningBenchmarkingNeural networks (Computer science).Electric transformers.Deepfakes.Disinformation--Prevention.Benchmarking.Stimulus generalization,Thesis