Know what's real with multimodal deepfake detection

Audio: MP3, OGG, AAC, G.711, G.723.1, PCMu, PCMa. Video: all major formats from current AI generators. Images: 250+ tools including GPT-4o, Gemini 2.0 Flash, and Flux v2.

DETECT-World is the first deepfake detector based on world model architecture. It expands on DETECT-3B Omni's foundation, reviewing both known generation patterns and physical consistency, including such things as lighting, motion, and continuity. This means it can flag content faster from a generator it hasn't seen before.

DETECT-World's architecture makes it faster to train against new generators than only using pattern based recognition. Its checks for physical consistency give it a head start on day-zero coverage. It doesn't rely on memorization alone: it also reviews whether content matches how the real world behaves.

Yes. Every detection surfaces the specific artifacts, patterns, and anomalies that triggered the flag, covering context, intent, and liveness. Dig deeper through an AI-powered chat interface, and every finding generates a built-in audit trail your team can document and act on.

For regulated industries, the primary barrier to adopting cloud-based detection is the legal risk of media storage. Zero Retention Mode ensures submitted media is permanently deleted after analysis and never viewable by Resemble staff or systems.

Built to reduce false positives, reverse image search runs alongside detection and searches the web for matching images to provide additional context on whether a submission is real or fake. It surfaces historical evidence predating AI models, traces the original unedited source to confirm face or background swaps, identifies zero-day fakes with no historical web record, matches against known fact-checked hoaxes, and shows how widely an image has spread across the internet.