NVIDIA launches AI tool to detect deepfake videos in 22 milliseconds with 92% accuracy

NVIDIA's new AI can detect deepfake videos in just 22 milliseconds

NVIDIA has a new AI tool that can tell fake videos from real ones in milliseconds As generative AI becomes increasingly capable of producing videos that are nearly indistinguishable from real footage, the race is no longer just about creating synthetic media. It's about detecting it before it spreads. At SIGGRAPH 2026, NVIDIA unveiled Synthetic Video Detector, a new AI-powered verification tool designed to identify AI-generated videos with remarkable speed and accuracy. Rather than replacing traditional fact-checking or forensic analysis, the company says the technology is intended to give newsrooms, broadcasters and enterprises another layer of confidence before synthetic videos enter the public domain. Recommended Videos The announcement comes at a time when deepfake videos are becoming increasingly realistic, making it harder for both people and automated systems to determine what's authentic. Whether it's manipulated political speeches, AI-generated celebrity clips or fabricated news footage, synthetic media has rapidly evolved from an internet curiosity into a genuine challenge for journalism, cybersecurity and public trust. NVIDIA wants AI to fight AI-generated misinformation Synthetic Video Detector is being introduced as part of NVIDIA's NIM microservices, allowing organizations to integrate AI-powered video verification directly into existing workflows rather than building entirely new moderation systems. The system examines videos frame by frame and assigns a probability score indicating whether the footage has been generated or manipulated using AI. According to NVIDIA, the detector can process a 1080p video in as little as 22 milliseconds on RTX systems, making it fast enough for real-time or near-real-time analysis in production environments. Performance is another headline feature. NVIDIA claims the detector achieves up to 92% accuracy on uncompressed video, with accuracy falling to 87% on videos compressed by 15% and 82% when compression reaches 50%. Compression remains one of the biggest challenges for deepfake detection because platforms like YouTube, TikTok, and Instagram routinely compress uploaded videos, often removing subtle visual artifacts that detection models rely upon. The company also says the latest version ranks at the top of the AI GVD Bench, an industry benchmark used to evaluate synthetic media detection systems, suggesting it performs competitively against existing open-source and commercial alternatives. The benchmark chart shown in NVIDIA's presentation highlights the detector outperforming many established models across multiple AI video generators. Detecting deepfakes is becoming just as important as generating them The launch reflects a broader shift taking place across the AI industry. Over the past two years, companies have invested heavily in video generation models capable of producing photorealistic clips from simple text prompts. While these systems have unlocked new creative possibilities for filmmaking, advertising and education, they have also dramatically lowered the barrier to creating convincing misinformation. For news organizations, the challenge is particularly acute. A single fabricated video shared online during an election, natural disaster or geopolitical crisis can spread globally before human fact-checkers have time to verify its authenticity. That's why verification tools are increasingly becoming as valuable as the generative models they're designed to detect. NVIDIA acknowledges that its detector isn't a silver bullet. The company says the system is intended to complement existing editorial verification processes rather than replace them. Human oversight, source verification and contextual reporting will remain essential, particularly as generative AI models continue to improve. Looking ahead, NVIDIA plans to integrate the Synthetic Video Detector into Wowza's Intelligence Video Framework, making the technology available across more than 35,000 deployments in 170 countries. As AI-generated video becomes cheaper, faster, and more convincing, the battle against misinformation is entering a new phase. Building better AI is only half the equation. The other half may be building AI capable of telling us when not to believe what we're seeing.

NVIDIA's Synthetic Video Detector Spots Fake News & AI-Generated Content With 92% Accuracy, Analyzing 1080p Footage In Just 22ms

NVIDIA is tackling "Fake News" with a new tool that is designed to help detect synthetic videos, called Synthetic Video Detector, which will be part of the NVIDIA NIM microservices. In Today's AI World, Distinguishing What's Real & What's Fake Is Becoming Harder & NVIDIA Is Solving This With Its Synthetic Video Detector NIM Microservice, Which Tackles "Fake News" With advancements in AI video generation, we're seeing videos that are indistinguishable from real video. While these videos have valuable use cases, they also pose a problem. If we cannot tell the difference between a synthetic video and a real one, it can erode public trust when videos are presented as news, as something that came from the real world. To address this concern, NVIDIA is leveraging its AI technologies, such as NIM microservices, so that these can also be used to detect when a video is real or synthetic. NVIDIA has announced Synthetic Video Detector NIM. It's a NIM microservice like any other, so it's very easy to deploy. The Synthetic Video Detector NIM analyzes videos frame by frame to produce a classifier score of whether it contains synthetic content or not. The Editorial teams can then use the data to prioritize clips for review, flag or quarantine questionable footage, or escalate them for deeper analysis. The Synthetic AI Detector NIM doesn't replace standard and established verification practices, but provides another layer of verification for time-sensitive decisions. According to NVIDIA, the NIM offers model accuracy of up to 92% on uncompressed video, 87% at 15% compression, and 82% at 50% compression. This NIM microservice can process 1080p video in as little as 22ms on NVIDIA RTX systems and around 30ms on NVIDIA's L40 GPUs. The latest model revision has also shown improved internal benchmark results, including AUC of 0.9614 and accuracy of 0.9453 on the internal NVIDIA test set. AUC (Area Under the Curve) measures how well a classifier ranks positive samples above negative ones, independent of thresholds. Thresholds can be configured to support different review postures, including more conservative settings that prioritize reducing the chance that synthetic video is missed. Synthetic Video Detector tool is already topping the leaderboards on the AI GVD bench. NVIDIA is working with Wowza to embed the microservice in its Intelligence Video framework, and it will soon be available to over 35,000 deployments across 170 countries. Follow Wccftech on Google to get more of our news coverage in your feeds.