International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Detection of Deepfake Video’s Using Long-Distance Attention |
|---|---|
| ABSTRACT | Deepfake videos are videos that are created or manipulated using Artificial Intelligence (AI) and deep learning techniques. As these technologies continue to develop, it has become increasingly difficult to distinguish fake videos from real ones. The growing use of deepfake technology has led to serious concerns such as misinformation, identity theft, digital fraud, and cybersecurity threats. Therefore, developing an accurate and reliable deepfake detection system has become an important research area. This research proposes a deepfake video detection system that combines YOLOv8 with a Long-Distance Attention model. The uploaded video is first divided into individual frames and pre-processed before being passed to the YOLOv8 model for accurate face detection and extraction. The detected facial regions are analyzed to identify important features such as facial texture, eye and lip movements, and manipulation artifacts. These features are then processed by the Long-Distance Attention model, which analyzes both spatial features and long-range temporal relationships across multiple frames. Finally, the extracted features are classified using a Fully Connected Layer and a Softmax classifier to determine whether the uploaded video is Real or Deepfake. The proposed system improves the accuracy and reliability of deepfake detection by combining accurate face detection with temporal feature analysis. This approach helps enhance the authenticity, security, and trustworthiness of digital media. |
| TITLE | |
| AUTHOR | BHAVANI B E, LAKSHMITHA H Y Dept. DoS in Computer Science, SBRR Mahajana First Grade College (Autonomous), PG Wing, Pooja Bhagavat Memorial Mahajana Education Centre, Mysore, Karnataka, India |
| VOLUME | 187 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1408006 |
| pdf/6_Detection of Deepfake Video’s Using Long-Distance.pdf | |
| KEYWORDS | |
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