Analysis Approach
Our visual analysis is based on an efficient convolutional neural network architecture designed to detect fine-grained visual inconsistencies across video frames. The system examines temporal patterns and spatial artifacts that may indicate synthetic generation.
Visual Analysis
Convolutional neural network trained on diverse datasets of authentic and synthetic video content.
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.
Timely Analysis
Optimized for timely analysis while maintaining consistent evaluation quality across submissions.
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.
Evaluation
Evaluated Accuracy
Our system achieves high accuracy across a diverse evaluation dataset, with performance varying by content type and quality. Results were measured on standard research benchmarks including FaceForensics++, Celeb-DF, and DFDC.
Performance varies by manipulation type:
- Face Swap: Strong detection
- Face Reenactment: Strong detection
- Audio-Driven Synthesis: Moderate detection
- Full Synthetic (SORA, Veo): Active development
Training Data
The model was trained on established research datasets:
- FaceForensics++: Benchmark dataset with multiple manipulation methods
- Celeb-DF: High-quality synthetic face dataset
- DFDC: Deepfake Detection Challenge dataset
Limitations: Detection accuracy depends on video quality, compression, and generation method. Newer AI video generators (SORA, Veo 3) present ongoing detection challenges. Results should be interpreted as indicators, not definitive proof.
Try the Analysis Tool
Submit a video for evaluation
Analyze Video