Deepfake Video Detector
Identify AI-generated and manipulated videos with frame-level evidence, confidence scores, and explainable signals. Built for KYC, content moderation, and verification workflows.
What We Detect
Comprehensive video manipulation and deepfake detection
Face Swaps
Detect replaced faces in videos with temporal consistency analysis across frames
Lip Sync Manipulation
Identify mismatched audio and lip movements using cross-modal analysis
Generative Video
Detect fully AI-generated video content from diffusion and autoregressive models
Face Reenactment
Identify puppeteering and facial reenactment attacks with motion analysis
Splicing & Editing
Detect edited, spliced, or composited video segments with temporal forgery detection
Deepfake Artifacts
Identify subtle generation artifacts like flickering, blurring, and inconsistency patterns
Explainable Evidence
Understand exactly why content was flagged
Evidence Types
- โFrame-level bounding boxes for detected manipulations
- โTemporal inconsistency heatmaps across video timeline
- โConfidence bands with calibrated probability estimates
- โSignal breakdown (lip-sync, temporal, artifact analysis)
Performance
- โกSub-2 second average response time
- ๐Batch processing for high-volume workflows
- ๐ฏ94%+ accuracy on in-the-wild dataset (see benchmarks)
API Integration
Get started in minutes with our unified API
curl -X POST https://api.zerotrue.app/v1/detect \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"modality": "video",
"url": "https://example.com/video.mp4",
"options": {
"include_evidence": true
}
}'Limitations & Best Practices
Understanding model constraints for optimal results
False Positive Risk
Highly compressed videos, low-quality footage, or heavy motion blur may increase false positive rates. Use confidence bands to set appropriate thresholds for your use case.
Adversarial Attacks
Sophisticated adversarial techniques may evade detection. We continuously update models against emerging threats. Review benchmarks for latest robustness metrics.
Generalization Bounds
Model performance may vary on novel generation methods or unusual video styles. We provide rolling quarterly evaluations to track real-world performance.