VeriVid:AI-Powered Video Integrity and TrustSystem
Tagline: Empowering Trust in Video. Description: VeriVid leverages AI and blockchain to verify credentials, ensure attribution, and detect tampering, fighting misinformation in the digital age.
Created on 2nd October 2024
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VeriVid:AI-Powered Video Integrity and TrustSystem
Tagline: Empowering Trust in Video. Description: VeriVid leverages AI and blockchain to verify credentials, ensure attribution, and detect tampering, fighting misinformation in the digital age.
Describe your project
In-Scope:
Verification of Creator Credentials: Blockchain will be used to securely verify and track the credentials of video creators and editors, ensuring authenticity and traceability.
Attribution Tracking: The system will track the source of video content through metadata and content hashing, providing clear attribution and recording any modifications.
Tampering Detection: AI algorithms will analyze videos for signs of tampering, such as deepfakes or unauthorized edits, ensuring content integrity.
Out of Scope:
Non-Video Content: The focus is solely on video content, excluding text and images.
Real-Time Streaming: The solution will not cover live streaming verification, focusing instead on pre-recorded content.
Legal Penalties: This project will not handle legal enforcement related to misinformation.
Future Opportunities:
Live Streaming Verification: Expanding to real-time content verification.
Social Media Integration: Partnering with platforms to enhance content trust.
Creator Tools: Developing user-friendly verification tools for content creators.
AI Evolution: Keeping up with advancing deepfake technology to improve detection.
This solution aims to ensure trust in video content, helping to combat misinformation and promote transparency.
Challenges I ran into
Building the VeriVid project came with several challenges, each requiring innovative solutions:
- Integrating Blockchain Technology
Challenge: Understanding smart contracts and decentralized ledgers was overwhelming.
Solution: Conducted extensive research and built small prototypes to gain hands-on experience. - Deepfake Detection Accuracy
Challenge: Initial AI models had low accuracy, leading to false positives and negatives.
Solution: Collected diverse datasets and collaborated with experts to improve model training and enhance accuracy. - Metadata Standardization
Challenge: Different platforms had unique metadata structures, complicating uniform tracking.
Solution: Researched metadata standards and developed a middleware solution to standardize data across platforms. - User Interface Design
Challenge: Creating an intuitive UI that balances complexity and usability was difficult.
Solution: Employed user-centered design principles and conducted usability tests to streamline the interface. - Performance Optimization
Challenge: High-resolution video analysis was resource-intensive, affecting performance.
Solution: Implemented multi-threading for parallel processing and leveraged cloud computing for scalability.
Conclusion
Each challenge presented valuable learning experiences that strengthened the project and enhanced my problem-solving skills for future endeavors.
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