Overview
Reality Defender provides enterprise-grade deepfake detection across text, image, audio, and video in real time. It uses an ensemble of blended models to flag AI-generated and manipulated media before it can deceive people or systems.
Our take
Reality Defender detects synthetic audio, images and video for enterprise use, and the free API tier makes evaluation possible. Deepfake detection is a harder problem than its marketing suggests: it is an arms race where detection lags generation, and a negative result is weak evidence. Useful as one signal in a process, dangerous as a single point of decision.
Worth it if
Financial services, media and identity verification adding a synthetic-media signal to existing checks.
Look elsewhere if
Any workflow that would treat a detection result as conclusive on its own.
✨ Features
Multi-Modal Detection
Detects manipulation across text, image, audio, and video.
Real-Time Scanning
Flags deepfakes live in calls, meetings, and access workflows.
Model Ensemble
Blends many models for resilience against evolving deepfakes.
Enterprise Integration
Integrates with existing applications via a platform-agnostic API.
⚖️ Pros & Cons
Pros
Cons
💰 Pricing
Free
$0 /mo
- 50 audio or image scans per month
- API access for deepfake detection
- Get started without sales contact
Enterprise
Custom
- Contact sales for a quote
- Real-time deepfake detection across audio, image, video, text
- Industry and use-case specific solutions
- Higher volume scanning and integrations
Categories
❓ Frequently Asked Questions
How reliable is deepfake detection?
Better than chance and not conclusive. Detection trails generation by design — new generators produce artefacts detectors were not trained on. A positive result is a strong signal worth investigating; a negative result mostly means this detector did not find evidence. Neither should be the sole basis for a decision about a person.
Is provenance a better approach than detection?
For establishing authenticity, yes. Content credentials such as C2PA attach cryptographic provenance at creation, which proves what something is rather than guessing what it is not. The limitation is that metadata can be stripped and its absence proves nothing, so in practice the two approaches complement each other.