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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM47208
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
With the rapid advancement of deep learning techniques, the generation of synthetic mediaâcommonly 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.
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Publication Date
5/7/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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