Deep-Fake Visual Detection using AI

Abstract

In an era where synthetic media is becoming increasingly sophisticated, this project introduces an advanced AI-powered 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.

Introduction

The project addresses the growing threat of deepfake technology—AI-generated or manipulated images and videos that convincingly mimic real content but spread misinformation, threaten privacy, and undermine public trust. To combat this, the system uses two advanced AI models: YOLOv8 for real-time image deepfake detection and ViViT, a transformer-based model, for analyzing spatial and temporal features in videos. Both models are integrated into a user-friendly Flask web interface that allows easy media uploads and provides clear authenticity results with confidence scores.

The system targets practical applications like media verification, social media monitoring, digital forensics, and public awareness. It is designed for speed, accuracy, and accessibility, making it suitable for both technical and non-technical users. Future plans include adding audio deepfake detection and real-time live stream analysis.

A literature survey highlights the evolution from CNN and RNN methods to transformer-based models like ViViT and improvements in YOLO for fast, accurate detection. The project’s results demonstrate high accuracy (~91-93%) in detecting deepfakes in images and videos with efficient processing times, confirming the system’s robustness and usability.

Conclusion

In an age where digital content can be easily manipulated and disseminated within seconds, the rise of deepfake technology has introduced serious concerns around misinformation, privacy invasion, and media trust. This project addressed these challenges by developing an AI-powered solution capable of detecting deepfake content in both images and videos. The system effectively integrated two state-of-the-art models—YOLOv8 for image-based detection and ViViT for video-based analysis. YOLOv8 provided fast and accurate image forgery detection, while ViViT demonstrated strong capabilities in capturing both spatial and temporal inconsistencies in video sequences. These models were deployed within a user-friendly Flask web interface, enabling seamless interaction and real-time feedback for users without requiring technical expertise.

References

[1] Afchar, D., Nozick, V., & Yamagishi, J. (2018). MesoNet: a Compact Facial Video Forgery Detection Network. arXiv preprint arXiv:1809.08548. [2] Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems (NeurIPS), 27. [3] Rossler, A., Cozzolino, D., Thies, J., Martherus, A., Zollhofer, M., & Nießner, M. (2019). FaceForensics++: Learning to Detect Manipulated Facial Images. arXiv preprint arXiv:1901.08971.

Copyright

Copyright © 2025 Sandesh Dhage, Ashish Bhosale , Anjali Ingale , Nayana Jadhav , Dr. B. S. Borkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.