Unified Deepfake Detection System for Image, Video and Audio Media

Author(s)	Prof. Neelakantappa T T, Ms. Ashwini J, Ms. Ashwini H, Ms. Lavanya K, Ms. Sushmitha D
Country	India
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.
Keywords	Deepfake Detection, Machine Learning, Computer Vision, Audio Analysis, LSTM, Deep Learning
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-29
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.61159

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research