Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content

Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content

Prof.Deepak Naik

Assistant Professor,Department of Computer Science & Engineering,MIT,ADT

Maharashtra Institute of Technology, Arts Design & Technology(MIT ADT) Pune,India deepak.naik@mituniversity.edu.in

Mr.Aditya Inamdar

Department of Computer Science & Engineering,MIT,ADT

Maharashtra Institute of Technology, Arts Design & Technology(MIT ADT) Pune,India inamdar.s.aditya@gmail.com

Mr.Yash Sonawane

Department of Computer Science & Engineering,MIT,ADT

Maharashtra Institute of Technology, Arts Design & Technology(MIT ADT) Pune,India yashsonawane0144@gmail.com

Mr.Yash Pandey

Department of Computer Science & Engineering,MIT,ADT

Maharashtra Institute of Technology, Arts Design &

 Technology(MIT ADT) Pune,India yashrajpandey2005@gmail.com

Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framework demonstrates competency in detecting video manipulations as well as synthesized speech and AI- generated images. Our approach the detection framework links deep neural networks known as CNNs together with Transformers are combined with CNNs to identify discrepancies between several input modalities which results in better detection precision. The implementation includes Explainable AI (XAI) techniques for our framework. The approach enhances model interpretability by identifying major traces of forgery through XAI techniques. artifacts such as unnatural facial expressions, lip-sync mismatches, and audio waveform abnormalities. Self- supervised learning with a built-in detection of evolving adversarial attacks is integrated in our system model. Through its learning capability the system develops the ability to handle newly emerging techniques. deepfake generation techniques without explicit retraining. The proposed work introduces a blockchain-based system for forensic purposes. A system offering content authenticity through secure metadata verification of media files enables forensic verification of data authenticity. Our experimental results demonstrate a significant improvement in detection accuracy and these deepfake detection models outperform other standalone deepfake systems due to their enhanced robustness capabilities. This study creates foundations which enable real-time implementation. scalable, and explainable deepfake detection solutions, crucial A network- based forensic system exists to fight against the mounting threats posed by AI-generated media. manipulation. Keywords— Deepfake detection, AI-generated media, Video forensics, Audio forensics, Explainable AI, Real-time detection, Blockchain authentication.