Challenges I Ran Into

Building TruthSeeker presented several significant technical challenges that required creative problem-solving and deep understanding of both zero-knowledge proofs and steganographic techniques.

1. ZKPassport Integration with Hyli Network

The Challenge:
Integrating ZKPassport zero-knowledge proofs with the Hyli blockchain network was one of the most complex hurdles. The main issue was that ZKPassport proofs are designed for Ethereum-compatible chains, but Hyli has its own unique architecture and state management system.

Specific Problems:

• ZKPassport SDK expects Ethereum-style transaction formats, but Hyli uses a different transaction structure
• Proof verification on Hyli required custom contract deployment and state commitment handling
• The proof data format needed to be adapted for Hyli's serialization requirements

How I Solved It:

• Created a custom ZKPassport contract specifically for Hyli that handles the proof verification logic
• Implemented a translation layer between ZKPassport proof format and Hyli's expected input format
• Used Hyli's state commitment system to properly store and verify proof data
• Built custom transaction handlers to bridge the gap between ZKPassport SDK and Hyli network

2. Steganographic Video Processing

The Challenge:
Implementing reliable steganographic embedding in video files while maintaining video quality and ensuring the embedded data could be reliably extracted was extremely challenging.

Specific Problems:

• Video compression algorithms would often corrupt or remove embedded steganographic data
• Finding the right balance between data capacity and video quality
• Ensuring cross-platform compatibility for video playback
• Creating a robust extraction algorithm that could handle various video formats

How I Solved It:

• Implemented a dual-layer steganographic approach using both border encoding and LSB (Least Significant Bit) techniques
• Used border encoding for primary data storage (more resistant to compression)
• Applied LSB steganography as a backup layer for additional data
• Created a custom video processing pipeline that preserves steganographic data during compression
• Built a robust extraction algorithm that can handle multiple video formats and compression levels

3. Real-Time Video Processing Performance

The Challenge:
Processing video files in real-time for steganographic embedding while maintaining a smooth user experience was a significant performance challenge.

Specific Problems:

• Large video files would cause browser freezing during processing
• Memory usage would spike during video analysis
• Processing times were too long for a good user experience

How I Solved It:

• Implemented a backend Python Flask server to handle heavy video processing
• Used Web Workers for frontend video analysis to prevent UI blocking
• Implemented progressive video processing with real-time progress feedback
• Added intelligent video sampling to reduce processing time while maintaining accuracy

4. State Management and Persistence

The Challenge:
Managing the complex state flow across the 5-step verification process while ensuring data persistence and proper error handling was crucial for user experience.

Specific Problems:

• Users would lose progress if they refreshed the page or navigated away
• Complex state transitions between different verification steps
• Need to handle partial completions and resume functionality

How I Solved It:

• Implemented comprehensive session storage for all verification states
• Created a robust state restoration system that could resume from any step
• Added proper error boundaries and recovery mechanisms
• Built a state validation system to ensure data integrity across steps