As generative AI continues to blur the line between what is real and what is artificial, the internet faces a growing trust deficit. Deepfakes are no longer an experiment, they are shaping opinions, elections, and global narratives. In a world where even the evidence can lie, ‘what to believe’ is now a technological question.
That is the space BitMind is trying to navigate. Built on decentralised AI infrastructure, the platform brings together developers around the world in an open competition to detect fake content faster than new models can produce it.
Founded in 2024, BitMind sits at the intersection of AI, blockchain, and public trust, using incentives and transparency to create what it believes is the most adaptive deepfake detection solution in the world.
A New Layer of Truth
BitMind’s tools are designed for both everyday users and enterprises. It started as a browser extension which is on the Chrome extension store.
“That’s free to use…when you hover over any image or video while you’re scrolling the internet, it will overlay whether the content you’re looking at is either real or AI-generated,” BitMind founder Ken Miyachi told AIM in an exclusive interaction.
The company also has a mobile app “which allows you to actually share any information, any piece of content from TikTok, Instagram, Facebook, X, etc.”
Once sent, BitMind will conduct an inferencing classification on it and tell you whether it’s real or not.
While the browser extension is completely free, the mobile app follows a freemium model.
Miyachi added that the mobile applications are free to use up to a certain number of inferences, post which a paid tier provides unlimited classifications.
Behind these tools lies a complex detection engine that blends multiple techniques. BitMind’s system starts with standard checks like C2PA watermarking, used by many large AI labs to tag generated media and comparisons with known fake content. “If it doesn’t hit any of those similarities, then it goes to one of our AI models,” Miyachi said.
Elaborating on the competitiveness of BitMind, Miyachi said that what distinguishes this tool is the fact that models and AI developers around the world compete in a real time scenario to create the best AI classification model.
A Decentralised Race Against Fakes
BitMind runs on BitTensor, a blockchain network that organises AI services into “subnets”.
“Bitmind is built off of a protocol called BitTensor. And BitTensor is a blockchain, which essentially is an economic system to create different AI services,” Miyachi said.
“Our vertical is in deep fake detection and understanding of essentially computer vision.”
Miyachi emphasised the transparency of their platform’s leaderboard, which openly displays the top-performing AI models. He highlighted the significant financial incentives available to AI developers who create superior models, as these models serve over 100,000 active users of their products.
He explained that a combination of a mixture of experts and an ensemble model, along with dynamic competition, leads to a highly accurate solution, surpassing any single model.
Enterprise clients are also testing the system. “We have a bunch of really exciting trials going on right now with different financial security companies,” Miyachi said, noting that generative AI is increasingly part of fraud and scam pipelines.
He went on to clarify that they work in close partnership with their enterprise clients to achieve this. He emphasised that regardless of the programming language or infrastructure, such as Python, JavaScript, Java, or even a simple curl call, integration into existing services is seamless.
BitMind’s early traction has also come from the AI agent and Web3 ecosystems, including integrations with Virtuals and ElizaOS.
According to Miyachi, the team is also exploring collaboration with social platforms such as X and Instagram, although direct partnerships are yet to be formalised.
He noted that the social media companies are protective of their data. But, the problem is growing too fast to ignore.
Trust and Compliance
About privacy, Miyachi emphasised compliance and encryption. “We’ve created enterprise SOC2 compliance services where enterprises can utilise our services, but we don’t store any of the data. When data is transferred, it’s all encrypted. There’s end-to-end encryption, and we have very robust logging and testing in place that doesn’t expose any of the underlying data.”
For Miyachi, deepfake detection isn’t about censorship—it’s about clarity. “There’s a general distrust of traditional media,” he said. “People deserve to know what’s real, and we’re building the tools to make that possible.”
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