Authentic Vision

Abstract

Deepfake manipulation has become a significant challenge in digital media. This research focuses on an advanced deepfake detection framework using Inception-ResNetv1 along with Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance interpretability. Our approach ensures high accuracy and explainability, allowing users to visualize decision-making areas in images. The system incorporates Multi-task Cascaded Convolutional Networks (MTCNN) for facial detection and alignment, improving overall performance. A Gradio-based UI enhances usability for technical and non-technical users. This paper outlines the architecture, implementation, and advantages of our method.

Introduction

The rise of artificial intelligence and machine learning has enabled deepfake technology, which creates highly realistic but fake videos and images using techniques like GANs and autoencoders. While deepfakes have useful applications in entertainment and education, they also pose serious risks including misinformation, political manipulation, identity theft, and fraud. Detecting deepfakes is critical, especially as they spread on social media and news platforms.

Current detection methods use deep learning models such as CNNs, Vision Transformers, and frequency analysis to identify subtle inconsistencies in manipulated media. However, challenges remain including poor generalization to new deepfake types, vulnerability to adversarial attacks, limited datasets, high computational costs, and privacy concerns.

This research proposes a robust deepfake detection system using Inception-ResNet-v1 combined with Grad-CAM visualizations to enhance accuracy, transparency, and interpretability. A user-friendly interface built with Gradio supports accessibility for researchers and professionals.

Applications extend beyond media authentication to cybersecurity, law enforcement, education, and digital credential verification. The study highlights the importance of continuous model updates, ethical considerations, and integrating detection tools into legal and digital frameworks to combat deepfake misuse effectively.

Conclusion

Deepfake technology is evolving at an unprecedented pace, posing significant risks to media integrity, cybersecurity, and personal privacy. As generative AI models become more sophisticated, the need for robust and adaptable deepfake detection mechanisms has never been greater. A. Key Takeaways from This Study AI-driven Deepfake Detection is Essential for Digital Security • Advanced detection models are crucial for protecting news media, legal proceedings, and social platforms from misinformation. • Deepfake fraud in banking, identity verification, and corporate communications requires continuous monitoring. Hybrid Approaches Offer the Best Protection • Combining deep learning, forensic analysis, and blockchain authentication results in higher detection accuracy. • Hybrid detection methods can identify GAN-generated inconsistencies, motion artifacts, and physiological cues (e.g., pulse detection). Ethical Considerations Must Be Addressed • While deepfake detection enhances security, it raises concerns about privacy invasion, potential misuse by governments, and AI biases. • Implementing regulatory frameworks and AI ethics guidelines is critical for responsible deployment. Future of Deepfake Detection: AI-Augmented Digital Trust • Real-time deepfake detection will become standard in social media platforms, video conferencing, and content verification. • Blockchain-based authentication and cryptographic video signatures will help prevent AI generated misinformation.

References

Citations of key research papers, articles, and use of OpenApi such as ChatGPT, Claude, Gemini are used in the model development. Other such references used are listed below: [1] DeepFaceLab github - https://github.com/iperov/DeepFaceLab [2] DFaker github - https://github.com/dfaker/df [3] faceswap-GAN github https://github.com/shaoanlu/faceswap-GAN [4] face swap GitHub - https://github.com/deepfakes/faceswap [5] FakeApp - https://www.malavida.com/en/soft/fakeapp [6] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi and Siwei Lyu. CelebDF: ALarge-scale Challenging Dataset for DeepFake Forensics research paper. [7] Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, and Mario Fritz. Natural and effective obfuscation by head inpainting. In CVPR, 2018. [8] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In CVPR, 2015. [9] Justus Thies, Michael Zollhofer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. In SIGGRAPH, 2019.

Copyright

Copyright © 2025 Nityam Bhargava, Abhishek Rawat, Pranav Tyagi, Neelansha Agarwal. 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.