Deepfakes, AI-generated media that manipulate videos, images, and audio, have become a significant challenge in today's digital world. With advancements in deep learning, it has become increasingly difficult to distinguish between real and fake content. However, AI-driven detection techniques are evolving to combat this growing threat.
Project Overview
We built this project during the Hackathon, focusing on AI-powered deepfake detection. This was developed during the rise of AI-generated images and videos. Our platform primarily worked on image, video, and audio deepfake detection by leveraging fine-tuned AI models. We used datasets from Hugging Face and trained our models to enhance accuracy.
Technology Stack
Frontend: React.js, Tailwind CSS
Backend: Node.js, Express.js
Machine Learning: TensorFlow, PyTorch
Database: PostgreSQL
API & Model Integration: FastAPI for processing uploaded media
Cloud & Deployment: AWS Lambda for serverless processing, Vercel for frontend deployment
How It Works
Users upload an image, video, or audio file to the platform.
The file is sent to the backend and processed by AI models.
The system analyzes inconsistencies in facial features, unnatural movement patterns, and voice anomalies.
The platform then returns a probability score indicating the likelihood of the file being a deepfake.
Challenges Faced
Evolving Deepfake Techniques: Constant advancements in deepfake generation make detection more difficult.
Computational Complexity: High model training times for fine-tuning improved accuracy.
False Positives: Ensuring minimal misclassification of real media as deepfake.
Future of Deepfake Detection
The future of deepfake detection will rely on:
Multimodal AI Models: Combining visual, audio, and contextual analysis for better accuracy.
Blockchain for Verification: Secure, immutable records for media authenticity.
Real-Time Detection Systems: AI-powered solutions for live deepfake monitoring.
Conclusion
Our project showcased the power of AI in detecting deepfakes while also highlighting the risks they pose. As deepfake technology evolves, so must our detection methods. With continuous advancements in AI, improved datasets, and stronger computational power, we can enhance digital trust and safeguard against misinformation.