BitMind

BitMind is a decentralized deepfake detection and AI content verification platform operating as Subnet 34 (SN34) on the Bittensor network. The platform aims to build the most accurate fully-generalized system for detecting synthetic and semi-synthetic media, including AI-generated images, video, and audio, regardless of their content or what model generated them. BitMind reports 95% accuracy on real-world deepfake content with sub-second response times, and states it serves over 100,000 monthly active users across more than 100 countries.

The subnet's core architecture is called GAS (Generative Adversarial Subnet), inspired by Generative Adversarial Networks (GANs). Detectors and generators compete in a continuous loop: detection miners sharpen their ability to spot synthetic media, while generative miners push to create more convincing fakes. This adversarial process drives improvement on both sides and continuously generates the training data needed to keep detection capabilities ahead of evolving threats.

BitMind offers multiple products built on top of the subnet's detection capabilities. These include a web-based detection tool at bitmind.ai/detect, a Chrome extension, a mobile app, Discord and Telegram bots, and an API with 100 free scans per month. For enterprise clients, BitMind provides custom solutions with dedicated SLAs, SOC 2 compliance, zero data retention, GDPR/CCPA alignment, and TLS 1.3 encryption.

BitMind operates with two types of miners. Discriminative miners submit detection models that are evaluated against a wide variety of real and synthetic media and rewarded based on accuracy. This model reduces the capital required to mine and allows the subnet to reliably identify unique contributions rather than rewarding speed of registration. Generative miners take the opposite role: they generate and modify media according to prompts from validators and are rewarded based on their ability to pass validation checks and fool the discriminative miners. Validators challenge and score both miner types, presenting them with a balanced selection of real and synthetic content.

For each challenge, a discriminative miner receives an image or video and must respond with a multiclass prediction indicating whether the media is real, fully AI-generated, or partially modified by AI (semi-synthetic). The foundation detection model draws from a 2024 CVPR paper on Neighborhood Pixel Relationships for generalizable deepfake detection. The team also developed CAMO, a gating mechanism that dynamically routes content-specific images to the appropriate specialized detectors. Application requests from end users are distributed to validators through an API server and load balancer, connecting the subnet's detection output to all consumer and enterprise products.