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@article{178673,
        author = {Harish T S and Tejaswini E and Diwan G and Dhanusha K S},
        title = {Detection of AI-Generated Videos Using Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5506-5511},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178673},
        abstract = {The Deepfake technology poses a growing threat to digital security, media integrity, and the fight against misinformation. This study presents a deepfake detection framework built on Convolutional Neural Networks (CNNs) and trained using the Celeb-DF dataset. The model is designed to analyze facial features and detect subtle inconsistencies caused by deepfake manipulation. A structured preprocessing pipeline, including face detection, alignment, and normalization, enhances feature extraction for improved accuracy. The CNN-based model efficiently captures spatial patterns and artifacts that distinguish fake content from real images. Performance evaluations confirm the model’s effectiveness in accurately classifying deepfake and authentic faces. The study underscores the potential of CNNs in deepfake detection while paving the way for future advancements, such as incorporating multi-modal analysis and real-time detection systems. This project is seek to elevate digital trust and addressing the growing concerns surrounding synthetic media.},
        keywords = {Deepfake Detection, Convolutional Neural Networks, Celeb-DF Dataset, Facial Feature Analysis, Digital Forensics, Image Classification, Synthetic Media, AI Security, Misinformation Prevention, Computer Vision, ResNet, ViT Transformers, Frame Extraction, Face Recognition, Assembling Method.},
        month = {May},
        }