Hossain, M IqbalJami, Syed HasnadAlam, MD.Tanzeem UlAzrin, AlponaIstiaque, S.M. Atahar2025-06-242025-06-2420252025ID 20301236ID 20301228ID 21101135ID 20301237http://hdl.handle.net/10361/26269Cataloged from PDF version of thesis.Includes bibliographical references (pages 61-63).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025Artificial Intelligence (AI) generative technologies such as DALL·E 3, DALL·E 4, Stable Diffusion, MidJourney, and Generative Adversarial Networks are developing quickly, making it more difficult to distinguish between real, synthetic, and AI-generated (AIG) images. As these technologies progress, traditional detection methods and datasets have become outdated, leading to poor detection accuracy and inference time, particularly for video content. Our research introduces an efficient pipeline for real-time video analysis (RTVAS) to overcome these issues in an AI-generated content detection (AIG-CD) system. We created our own dataset with the help of newly developed generative models for robust training. Our multi-stage processing pipeline includes preloading the detection model (SAAT-ResNet50-3BD) at startup, real-time frame capture, adaptive preprocessing methods to speed up inference time, and face detection to focus on relevant regions. Our proposed model uses pre trained weight and includes enhanced feature extraction, attention mechanism to find any artifacts and irregularities with proper regularization techniques to prevent overfitting that distinguishes between AI-generated (AIG), synthetic media (SM) photos altered by real people. The classification decision is obtained by combining each frame’s frequency and statistical average forecasts. As a scalable and effective detection system, our proposed model outperforms Xception (93.6%), EfficientNetV2 (96.5%), MobileNetV3 (94.3%), and VGG16 (65.9%) models in terms of performance matrices. With a detection accuracy of 98.5%, our proposed model offers a superior balance between detection accuracy and compute efficiency, making it highly suitable for practical applications in media forensics, content regulation, and deepfake detection in video frames (VDF).63 pagesenBRAC University theses reports 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.Synthetic media detectionDeepfake detectionTransfer learningReal-time video analysisResNet50Artificial intelligenceArtificial intelligence.Data mining.Deepfakes.Thesis