Developing DeepScan came with several technical and operational challenges, from AI accuracy to real-time processing and user privacy. Here are some of the key hurdles we faced:

1️⃣ AI Model Accuracy & Performance
Training deepfake detection models to maintain high accuracy while minimizing false positives.
Balancing model efficiency with real-time analysis to ensure fast and precise deepfake detection.
Handling diverse datasets to detect various forms of manipulations, including AI-generated audio, synthetic videos, and altered images.

2️⃣ Real-Time Processing & Optimization
Implementing TensorFlow Lite for mobile devices while maintaining accuracy without heavy computational power.
Optimizing deep learning algorithms to work efficiently across mobile, web, and desktop platforms.
Ensuring low-latency detection for instant feedback on media authenticity.

3️⃣ Privacy & Security
Ensuring all media is processed locally to prevent data leaks or privacy violations.
Implementing robust encryption and security protocols for enterprise-grade protection.
Avoiding third-party dependencies that could compromise user data privacy.

4️⃣ Cross-Platform Compatibility
Developing a Flutter-based UI that works seamlessly across Android, iOS, web, and desktop.
Integrating FastAPI backend with AI models for smooth and scalable performance.
Handling device-specific optimizations to ensure DeepScan runs efficiently on low-end and high-end devices.

5️⃣ UX & Usability
Designing an intuitive Smart Screen Capture feature for easy region-based selection.
Balancing advanced AI features with a user-friendly interface for non-technical users.
Providing real-time feedback without overwhelming users with technical details.

🚀 Overcoming These Challenges
Through rigorous testing, model fine-tuning, and optimization, we built DeepScan to be a highly accurate, real-time, and privacy-focused media authentication suite, ready to combat digital deception.