Deepfake detection, replay attack prevention, and face swap identification — all in a single API call. FrameSentinel analyzes video KYC sessions in under 2 seconds with 99.7% accuracy.
As video-based identity verification becomes the standard, attackers are using increasingly sophisticated methods to bypass it.
Increase in deepfake attacks on KYC systems since 2023
Annual losses from identity fraud in financial services
Of traditional KYC systems fail to detect AI-generated videos
AI video fraud detection uses machine learning models to analyze video streams and identify signs of manipulation — including deepfakes, face swaps, replay attacks, and injected video feeds. It is a critical layer for any identity verification or KYC workflow that relies on video.
Identifies AI-generated or manipulated faces in video streams using neural network analysis of facial micro-expressions, skin texture, and temporal consistency.
Detects pre-recorded videos, screen recordings, and looped footage being presented as live verification sessions.
Identifies real-time face replacement attacks where an attacker overlays a different identity onto their own face during verification.
Detects virtual camera software and video stream injection tools that bypass the device camera entirely.
5 detection modules running in parallel, results in under 2 seconds
Upload a verification video via REST API. Supports MP4, AVI, MOV, WEBM up to 100MB.
Up to 15 key frames are extracted from the video for analysis.
5 detection models run simultaneously: deepfake, replay, injection, face swap, and metadata integrity.
Each frame receives individual scores. A weighted authenticity score and risk level are calculated.
Full results returned via API response or webhook — including frame-level timeline and detection flags.
Deepfake Detection Accuracy
Processing Time
Parallel Detection Modules
Frames Analyzed Per Video
Any platform that uses video for identity verification needs fraud detection
Protect customer onboarding from deepfake identity attacks. Comply with KYC/AML regulations while maintaining fast verification.
Prevent synthetic identity fraud and replay attacks during mandatory KYC verification for trading accounts.
Add AI fraud detection as a layer in your existing IDV pipeline. Integrate via API in minutes.
Verify seller and driver identities. Prevent account takeover and multi-accounting fraud.
Add video fraud detection to your KYC flow in 3 lines of code
// Upload video and get fraud analysis const result = await framesentinel.verify(videoFile); // Check results result.authenticity_score // 0.95 (0-1 scale) result.risk_level // "VERIFIED" | "SUSPICIOUS" | "REJECTED" result.detection_flags // { deepfake: false, replay: false, ... } result.frame_timeline // Per-frame analysis with timestamps
FrameSentinel detects deepfakes, face swaps, replay attacks, video injection, and metadata tampering. All 5 detection modules run in parallel on every video.
Average processing time is under 2 seconds. This includes frame extraction, parallel AI analysis across 5 models, and risk score calculation.
MP4, AVI, MOV, and WEBM formats are supported, with a maximum file size of 100MB per video.
No. Videos are automatically deleted immediately after processing. No permanent storage of user videos.
FrameSentinel provides a REST API and TypeScript SDK. Upload a video, receive a fraud analysis result. Most integrations take less than 30 minutes.
No. FrameSentinel provides authenticity scores, risk levels, and detection flags. Your system remains in control of the final approval decision.
Free trial includes 100 video analyses. No credit card required.
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