Our updated mobile app allows you to generate AND detect deepfakes in seconds. Create, verify, and explore AI-generated content all in one place. Try BitMind's AI Detector & Creator app here: bitmind.ai/mobile

Our new benchmarking survey paper is dropping soon. We rigorously tested deepfake detector benchmarks for the past 4 years. Very excited to share the results! Data-driven truth for AI security. Stay tuned.

The New York Times recently highlighted a challenge that affects everyone online: proving what's real in an era of increasingly convincing AI-generated content. As synthetic media improves, trust can no longer rely on intuition alone. Verification is becoming a necessity.

text detection is live on @bitmind x @ai_detection !

We're excited to announce our partnership with It's AI to advance text deepfake detection capabilities and strengthen our detection systems to include text. We're working toward a shared goal: making trust and verification more resilient in the age of generative AI. The future of digital trust depends on verification systems that can evolve as quickly as the threats they are designed to stop. Its AI has the leading AI text detection model and is now available through the BitMind API!

Forgery isn't new. What's new is how quickly AI can create convincing documents at scale. Most verification systems were built to catch human forgers. The next challenge is detecting machine-generated documents. BitMind now detects AI-generated documents.

Most AI detection benchmarks measure performance against known generators. Real-world environments are different. We evaluated our models against 300,000 images, including adversarial content specifically designed to evade detection. The goal was not to achieve a benchmark score. The goal was to understand how the system performs under pressure. Results like these are critical because attackers are constantly adapting. Detection systems need to adapt faster. 98% accuracy across a large-scale stress test is a reflection of that approach.

Modern influence operations are increasingly optimized for attention, emotion, and belief rather than technical compromise. AI is accelerating this shift. When persuasive synthetic content can be generated at near-zero cost, the attack surface expands from systems and networks to human judgment itself. The challenge is no longer just detecting manipulated content. It's preserving trust in environments where manipulation can be personalized, scaled, and continuously adapted. This is why cognitive security is rapidly becoming a critical discipline for governments, enterprises, and society at large.

Today we're locking 100% of our owner emissions. No selling, no taking value off the table. It's the clearest signal we can send about where we stand, so we want to explain the thinking behind it. We've spent 2.5+ years building in the Bittensor ecosystem. That's a long time in this space, and it reflects a real belief rather than a passing trade. We're convinced Bittensor is creating durable, long-term value, and we want to be fully aligned with that value as it compounds. Underpinning all of it is a principle we don't compromise on: AI must be beneficial to humans. Everything we build in AI security and detection serves that goal, keeping an increasingly synthetic internet one you can still trust. The timing matters. We're locking now because our revenue is starting to build and compound. This isn't a gesture made on hope, it's a decision made from strength. We believe we can grow a genuinely profitable business, one that generates token buybacks and lets us reinvest into more incentive, pushing the frontier of AI security and detection further with every cycle. That's the loop we're committing to: revenue funds buybacks, buybacks fund incentive, incentive advances the frontier. Locking our emissions is how we put our full conviction behind it. This isn't a bet on a market cycle. It's conviction in the work, and in the people doing it. We're here for the long game.

The frontier of AI has been generative, but we think the next unlock is different. Representation and embedding models are how detection moves forward, and they're the key to unlocking the next wave of physical AI use cases. Here's why it matters now. The world is automating fast, and every automated system opens a new attack vector for fraud. Return fraud is already the largest sector of global fraud in the world, and it's growing at a rapid pace. As AI agents start transacting and acting on our behalf, that surface only expands. Generation showed us what AI can create. Representation is how we'll understand, verify, and secure what it produces. That's why BitMind is building the horizontal security layer for AI, detection that sits across the stack and protects every system that automation touches.

Most people think detection is about classifiers. It's not. It's about representation, how a model sees an image before it makes any decision at all. Get the representation right and detection, generation, and embodied AI all get better downstream. It's where our research team is spending most of its time.

AI-edited photos of "damaged" deliveries. Generated images of missing items. Doctored receipts. Refund fraud is bleeding gig economy platforms. Users upload fake proof, the platform pays out, multiply by millions of orders. DoorDash, Uber Eats, Instacart all wear the cost. Image authenticity detection, built for the reimbursement flow.

We've spent the last several months rebuilding our detection stack around what our customers actually face in production: face swaps, synthetic identities, and human-likeness attacks aimed at verification and trust flows. Human-centric detection. Shipping now.

We’ve assembled a lineup of individual Subnet Keynotes from some of the most exciting teams building on Bittensor. Each keynote will spotlight a different part of the subnet economy: @micaelabazo, @metanova_labs — decentralised AI for pharma and drug discovery. @0xcarro, @TargonCompute — confidential compute infrastructure through TEEs. @tm0klc, @manakoai — vision models and Manako, turning Bittensor-powered computer vision into enterprise use cases. @mast3rdubs, @hippius_subnet — decentralised storage and cloud infrastructure. @kenjon, @bitmind — deepfake detection and AI content verification. @shardiban, @oroagents — the open arena for AI agents. June 2-3, Bittensor Track at Proof of Talk.

One question reaches our inbox every week. We built the answer. "Is this image AI-generated?" BitMind gives you more than yes/no. → Which model likely generated it → Which regions were manipulated → Confidence score your team can act on Free tier. No credit card. No demo call. Try it on the hardest image you have. If we get it wrong — we genuinely want to know. That's how the model improves.

CysecOnline, South Africa's trusted digital forensics experts, is now integrating BitMind into their services to detect deepfakes with industry-leading accuracy. Protecting clients from synthetic media, fraud & misinformation like never before. Real-time AI verification meets expert forensics. Secure what's real.

See our founder @kenjon talk @bitmind with the @twistartups !

The EU AI Act, Article 50: platforms must label AI-generated content starting August 2026. Non-compliance: fines up to 6% of global revenue. That's not a suggestion. That's a legal requirement for every platform serving EU users. Oftentimes this becomes global policy (e.g. EU car emissions) The infrastructure to detect and label this content at scale doesn't exist at most companies. We've spent 2 years building it.

Q1 2026 has been a breakout quarter for BitMind. We made significant strides toward our mission of creating a decentralized Trust Layer for the internet, delivering real product progress, enterprise traction, and ecosystem collaboration on the Bittensor network. Here is what we accomplished: Launched the Human Face Competition We kicked off our first major community-driven data initiative, the Human Face Competition. This open call is crowdsourcing diverse, high-quality face data to accelerate training of our specialized deepfake detection models and directly support our key partnerships. Formalized Strategic Partnership with Yanez (@yanez__ai)After months of technical integration, we officially partnered with Yanez to co-build a fine-tuned face deepfake detection model optimized for biometric-grade attacks. Yanez brings 20+ years of identity security expertise, patents, and a proprietary face dataset, while we contribute our proven AI-generated content detection model and Top 20 subnet infrastructure. This collaboration is already delivering a model neither subnet could build alone and is aimed squarely at the exploding deepfake fraud problem in crypto, finance, and identity verification. First Enterprise Customers and Revenue We closed our first enterprise contracts and generated real revenue from production deployments. These wins validate both the demand for our technology and our ability to serve serious customers who require reliability, compliance, and measurable performance. Complete Infrastructure Refactor We rebuilt our core infrastructure from the ground up. The result is major performance gains, dramatically improved scalability, and full readiness for SOC 2 certification with a strict zero-data-retention policy. This puts us in a strong position to meet the security and privacy standards enterprise and regulated customers demand. New Reporting Feature with Explainability We shipped a powerful new reporting dashboard that gives users clear, human-readable explanations for every detection decision. Transparency and trust are now built into the product, not added later.

been cooking @bitmind 🔥 big breakthrough: ensembles our top miners’ insanely diverse models (CNNs, SoTA ViT architectures, CLIP, VLM vision encoders + more) trained an attention layer on top. huge performance jump… and it nailed the in-the-wild vibe test. applied on images, videos coming soon new products + research report on the way 🫡