DETECT-World, introduced by Resemble AI, is a third-generation deepfake detection model that adds a learned understanding of physical reality to identify fakes. Unlike prior detectors that analyze pixels, frequencies, and biometric signals, DETECT-World also evaluates whether content is physically coherent, including lighting, geometry, motion, and audio-visual synchronisation. This approach addresses the problem documented by Vector Institute researchers in 2026 that performance drops sharply on content from newer generators. The article notes there are over 2.2 million AI model variants on Hugging Face, nearly doubling year over year. DETECT-2B, the first production-grade audio detector, used a Wav2Vec2 and Mamba-SSM ensemble architecture and achieved 94% accuracy across 30+ languages at 200ms latency. DETECT-3B Omni, the first multimodal step, had three billion parameters spanning audio, image, and video, with audio coverage of 51 languages and vision coverage of architectures including StyleGAN, DALL·E 3, Stable Diffusion, GPT-4o, and Veo 2. It ranked first on DFBench for image and speech detection, with EER dropping across every public benchmark tracked. DETECT-World asks both whether content matches known generator signatures and whether it violates a model of how physical reality works.