AiVidectOur AI Model
We use an advanced EfficientNet-B0 architecture enhanced with temporal attention mechanisms to analyze video sequences. This combination allows our model to detect subtle inconsistencies across frames that are characteristic of AI-generated content.
Deep Learning
EfficientNet-B0 backbone trained on millions of real and fake videos, optimized for both accuracy and speed.
Temporal Attention
Analyzes frame-to-frame consistency and temporal artifacts that deepfake generators struggle to maintain.
Multi-Modal Analysis
Examines facial movements, lighting inconsistencies, edge artifacts, and compression patterns.
Real-Time Processing
Optimized inference pipeline processes videos in under 30 seconds with cloud scalability.
Privacy-First
Videos are automatically deleted after analysis. We never store your content on our servers.
Probabilistic Scoring
Provides confidence scores rather than binary results, allowing for nuanced interpretation.
How It Works
Video Upload & Preprocessing
Your video is securely uploaded and preprocessed. We extract 16 uniformly sampled frames across the video duration to capture temporal information.
Frame-Level Feature Extraction
Each frame is analyzed by our EfficientNet backbone to extract deep visual features, focusing on facial regions, edges, and compression artifacts.
Temporal Consistency Analysis
Our attention mechanism analyzes how features evolve across frames, detecting unnatural transitions and temporal inconsistencies.
Confidence Scoring
The model outputs a probability score (0-100%) indicating the likelihood of AI manipulation. Scores above 80% suggest likely fake, below 40% suggest authentic.
Automatic Cleanup
After delivering your results, all video files are immediately deleted from our servers to protect your privacy.
Accuracy & Performance
Overall Detection Accuracy
Tested on benchmark datasets including FaceForensics++, Celeb-DF, and DFDC
Performance by Deepfake Type:
- Face Swap: 96% accuracy
- Face Reenactment: 93% accuracy
- Audio-Driven: 91% accuracy
- GAN-Generated: 97% accuracy
Training & Validation
Our model was trained on diverse datasets containing over 500,000 videos from multiple sources:
- FaceForensics++: Benchmark dataset with 5 manipulation methods
- Celeb-DF: High-quality celebrity deepfakes
- DFDC: Facebook's Deepfake Detection Challenge dataset
- Custom Dataset: Real-world samples from social media platforms
⚠️ Important Note: While our model achieves high accuracy on test datasets, deepfake technology is constantly evolving. We continuously update our model with new training data to maintain effectiveness against the latest generation techniques.
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