Describe your project

TrueVision is an AI-powered solution for detecting deepfake and manipulated video content, utilizing Convolutional Neural Networks (CNNs) and blockchain technology. It ensures video integrity by analyzing video frames for anomalies and storing tamper-proof metadata for attribution and origin tracking.

1. In-Scope of the Solution
   Deepfake Detection: Analyzes video content frame-by-frame using CNNs to identify altered or fake videos.
   Blockchain-Based Attribution: Tracks video origin and modification history through cryptographic hashes stored on the blockchain.
   Real-Time Alerts: Provides notifications when tampered content is detected, helping prevent the spread of misinformation.
2. Out of Scope of the Solution
   Audio Manipulation Detection: The current scope does not include detecting audio-based manipulations or voice deepfakes.
   Non-Video Media: Detection and attribution are limited to video content, excluding images or text-based media.
   Content Moderation: The solution does not handle content moderation or filtering beyond deepfake detection.
3. Future Opportunities
   Expanding detection to audio and other media types.
   Enhancing real-time detection for live-streamed content.
   Offering integration with content platforms for automated video verification at scale.

Challenges I ran into

Hurdle: Handling False Positives in Deepfake Detection
One significant hurdle encountered during the development of TrueVision was managing false positives in deepfake detection. Initially, the AI model flagged genuine videos as manipulated, especially in cases with complex lighting or fast movements, which caused confusion between real and fake content.

Solution: Improved Model Training and Data Augmentation
To overcome this, we took the following steps:

Enhanced Dataset: We expanded the training dataset by including more real videos with a variety of lighting conditions and fast movements, ensuring the model could learn to differentiate between natural variations and actual manipulations.

Data Augmentation: We applied techniques like rotation, flipping, and lighting adjustments to the dataset, making the model more robust to real-world variations in video content.

Fine-Tuning the Model: By adjusting the CNN architecture and applying transfer learning from pre-trained models, we significantly reduced the number of false positives while maintaining accuracy.

Through these improvements, TrueVision now achieves higher precision in identifying manipulated content without flagging genuine videos as fakes.