A 3-model ensemble analyzing every ingest feed at 6 inferences per second. Facial artifacts, temporal jitter, and frequency fingerprints — scored, alerted, and logged before a single manipulated frame reaches air.
Each frame is analyzed by three independent detection models. Their weighted outputs merge into a single confidence score between 0.0 (definitively real) and 1.0 (definitively fake).
Convolutional neural network trained on 2.4M synthetic face samples. Detects GAN artifact patterns including checkerboard upsampling, boundary blending seams, eye reflection inconsistencies, and tooth geometry anomalies that survive even high-bitrate encoding.
50% of composite score
Tracks 68 facial landmarks across consecutive frames, measuring inter-frame jitter that real physiology doesn't produce. Deepfake generators introduce micro-jitter in jaw contour, nasolabial folds, and hairline boundaries that compound across temporal windows.
30% of composite score
Applies 2D Fast Fourier Transform to detect upsampling signatures invisible to the human eye. GAN-generated faces leave distinctive spectral peaks from transposed convolution layers. These frequency-domain fingerprints persist even after compression and rescaling.
20% of composite score
Composite Score Formula
Weights are configurable per-stream via the API. Defaults tuned for broadcast-quality H.264/H.265 ingest.
Every analyzed frame produces a composite score. The gauge updates in real time, and alerts fire the moment thresholds are breached.
Frame analysis shows no synthetic indicators. All three models agree the content is authentic. No alert dispatched.
Inconclusive signals. Compression artifacts, unusual lighting, or encoding anomalies may trigger this zone. Monitoring escalated; human review recommended.
High-confidence synthetic content detection. Alert fires immediately via Redis pub/sub and webhook. Operator console highlights the affected feed. Frame-level evidence is logged.
The alert engine doesn't just fire on a single frame. It detects patterns across time windows, catching sustained attacks and rapid transitions that single-frame analysis would miss.
Fires when any single frame produces a composite score above 0.7. Immediate notification. No waiting, no averaging, no delay. If a single frame screams fake, operators know within milliseconds.
Detects a score jump greater than 0.3 within a 1-second window. Catches the moment a deepfake splice begins — even if the absolute score hasn't crossed the threshold yet. Critical for detecting mid-stream feed swaps.
Triggers when the composite score remains above the configured threshold for 5+ consecutive seconds. Distinguishes a real deepfake attack from momentary false positives caused by compression glitches or scene transitions.
Alerts publish to deepfake:alerts:{stream_id} channel. Subscribe from any service in your infrastructure for instant notification.
Configure HTTPS webhook endpoints per stream. Alert payloads include score, alert type, frame timestamp, and model-level breakdown. Retries with exponential backoff.
EDITORIAL
A deepfake of a head of state will appear on a live news broadcast. Not tomorrow — but sooner than the industry is prepared for. When it happens, the network carrying it will face a crisis of credibility that no retraction can undo.
The question is not whether it will happen. The question is whether your infrastructure will catch it before it reaches viewers.
Election cycles. Geopolitical tensions. Financial markets that move on a single screenshot. The incentive to produce a convincing deepfake for live broadcast has never been higher. The tools to create one have never been cheaper. A laptop with a consumer GPU can now generate face-swapped video at 30fps.
Most broadcast infrastructure has zero deepfake detection. None. The ingest pipeline trusts whatever arrives on the SRT/RTMP stream. If it looks like video, it goes to air.
V100 sits in the relay layer. Every frame is scored before it reaches the encoder. Every anomaly is flagged before it reaches the CDN.
This is not a post-hoc forensics tool you run after the damage is done. This is real-time detection at ingest speed, built into the same pipeline that handles your ABR packaging, DRM encryption, and CDN distribution.
Enable deepfake detection on any stream, configure thresholds, retrieve scores, and manage alert webhooks — all through the same V100 API you already use for broadcast.
The deepfake score submission path runs in 507 nanoseconds. That's the time from model output to indexed, queryable, alertable score. See the full benchmark breakdown.
View Benchmarks/v1/deepfake/enable
Enable deepfake detection on a live stream. Starts the 3-model ensemble on the ingest feed immediately.
/v1/deepfake/score/{stream_id}
Retrieve the current composite score and per-model breakdown for a live stream.
/v1/deepfake/config/{stream_id}
Update detection thresholds, model weights, and alert sensitivity per stream at runtime.
/v1/deepfake/alerts/{stream_id}
Retrieve the alert history for a stream. Filter by type, time range, and severity.
/v1/deepfake/webhooks
Register a webhook URL for real-time alert delivery. Supports HMAC-SHA256 signature verification.
/v1/deepfake/disable/{stream_id}
Disable deepfake detection on a stream. Stops model inference and alert dispatch. Score history is retained.
Explore the broadcast platform, benchmarks, and the full blog post on deepfake detection architecture.
96 microservices in a single Rust binary. AI Director, DRM, wagering sync, voice dubbing, and deepfake detection in one pipeline at 3.7us per tick.
8.3M broadcast ops/sec. 3.7us full pipeline. Deepfake score submission at 507ns. Verified on Apple M4 Max with Graviton4 extrapolation.
The full technical deep dive. 3-model ensemble architecture, scoring mechanics, alert strategies, and the threat landscape driving real-time deepfake detection.
Enable deepfake detection on your broadcast pipeline today. One API call. Three models. Every frame analyzed before it reaches a single viewer.