TruthSeeker

Demo

About

The problem it solves ## Description **TruthSeeker** is a privacy-first proof-of-video authenticity platform that embeds **cryptographic proof directly inside video files using steganography**. It combats deepfakes by offering three escalating tiers of identity verification: - **Anonymity** (ZKPassport Proof of Humanity), - **Pseudo-Anonymity** (Hyli Wallet Signature), - **Full Identity** (Hyli Protocol Identity Verification). It's especially useful for whistleblowers, citizen journalists, and **government officials** — anyone needing to prove **"I am a real human" or "I truly said this"** without relying on centralized platforms or trust. --- ## Problem Deepfakes are undermining trust in audio-visual content. In an era of AI-generated misinformation, there's no easy way to verify that a video came from a **real person** — let alone prove it came from a **specific, trusted source** like a government official or agency — **without relying on centralized authority or watermarking**. --- ## Solution TruthSeeker offers a **zero-trust**, decentralized way to **cryptographically bind the video to the identity of its creator**, based on their privacy preference: 1. **Anonymity** – Just proves the speaker is human (via ZKPassport nullifier). 2. **Pseudo-Anonymity** – Links the proof to a Hyli wallet address. 3. **Full Identity** – Connects the video to a **Hyli Protocol identity**, verifying that a real, verifiable identity made the statement. This is especially powerful in: - **Government contexts**, where officials can publish provably signed video announcements (e.g., public safety, legislation, emergency alerts), cryptographically proving their origin. - **Decentralized journalism**, where sources may wish to remain anonymous or pseudonymous but still provide irrefutable proof of authenticity. The proof is **embedded into the video** itself using **steganography**, making the video a **portable, self-verifiable artifact**. ---

How it's made

Challenges we ran into ## 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