A Unified Neural Framework for Real-Time Deepfake Detection Across Multimedia Modalities to Combat Misleading Content

Ayan Sar, Subhangi Sati, Tanupriya Choudhury, Purvika Joshi, Roohi Sille, K. Srihari, Kritika Bansal

Abstract: The rise of deepfake technology poses significant threats to the authenticity of content on social media. This research introduces Sach-AI, a pioneering framework for detecting various deepfakes in video, audio, and image data. Leveraging the power of deep neural networks, Sach-AI utilizes Eulerian Video Magnification combined with the ResNext architecture for enhanced detection. For video deepfakes, Long Short-Term Memory (LSTM) networks are integrated to improve classification tasks. This combination allows Sach-AI to effectively address the evolving, multimodal nature of deepfakes. The framework has been rigorously evaluated using diverse datasets, such as Celeb-DF and FaceForensics++, demonstrating its robustness and accuracy. Sach-AI achieved 97.76% accuracy in video deepfake detection, surpassing Intel’s FakeCatcher, 99.13% accuracy in audio deepfake detection, and 93.64% accuracy in image deepfake detection. These results underscore Sach-AI’s reliability in safeguarding digital media integrity against deceptive synthetic content in an era increasingly dominated by artificial technologies.