Year: 2025
Type: article
Abstract: : In an era where synthetic media is becoming increasingly sophisticated, this project introduces an advanced AIpowered solution designed to detect deepfake content in both images and videos. Deepfakes—media that has been digitally altered or artificially created using machine learning techniques—pose growing threats by facilitating the spread of misinformation, fabricating news content, and infringing on individual privacy. As these manipulated visuals become more convincing and widespread, the need for reliable detection methods becomes more urgent. To address this issue, the system leverages two state-of-the-art artificial intelligence models. For analyzing static visuals, it utilizes YOLOv8 (You Only Look Once, version 8)—a model renowned for its real-time object detection capabilities, blending both high speed and accuracy. YOLOv8 excels in scrutinizing image content to flag potential signs of tampering or fabrication. For video-based analysis, the system incorporates the ViViT (Video Vision Transformer) model. ViViT is designed to interpret not only the spatial characteristics within individual frames but also the temporal relationships between frames, enabling robust detection of manipulated video sequences. A user-friendly web interface built with the Flask framework in Python serves as the front end of the system. Users can upload media files—either images or videos—through the interface for authenticity evaluation. The system processes the input and displays the outcome along with a confidence score, indicating how certain the model is about its classification. The ultimate goal of this initiative is to provide an effective and easy-to-use platform that empowers users to authenticate digital media. As deepfake technology continues to evolve—especially across social media and digital journalism—such tools are essential for preserving the trustworthiness of visual information. Future enhancements may include support for detecting synthetic audio and implementing real-time detection for live video streams, broadening the system’s scope in combating digital disinformation.
Author: Sandesh Dhage
Institution: Amrutvahini College of Engineering
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410120057