Opinion: Ken Miyachi, founder of Bitmind
The centralized deep-fark detector is structurally shifted, brittle and lagging. The crypto industry requires encryption and native defense. Many independent model providers need a distributed detection network that catches real fakes and rewards many independent model providers to record their judgments.
The results: Transparency and synthetic use across exchanges, wallets, distributed finance (DEFI).
In the first quarter alone, $200 million was stolen through Deepfark scams, with over 40% of high-value crypto scams currently being attributed to spoofing generated by AI.
As criminals use deepfakes to bypass the KYC process and impersonate executives in fraudulent transfers, the crypto industry faces existential threats that centralized detection systems cannot resolve.
Intensive detection fails
The core obstacles are architectural.
Centralized detectors are competitively siloed, and vendor-locked systems optimally detect model output without anyone else. When the same company builds both generators and detectors, the incentives become blurry. These detectors are static and slow in contrast to their distributed counterparts, and train against last month’s tricks as they repetitive in real time by enemies.
Crypto cannot outsource this to the same closed system that deepens its out-pace without expecting the same pitfalls. It’s time to change that mentality and move to a distributed detection network.
Law enforcement agencies across Asia have dismantled 87 Deepfark fraud rings. This used AI-generated deepfakes to impersonate people like Elon Musk and government officials. The scam has evolved to include live Deep Fark spoofing during video calls. There, scammers pose as blockchain executives to greenlight fraudulent transactions.
For example, last year, Michael Saylor, chairman of Michael Saylor’s strategy executive, warned him to remove YouTube videos generated by around 80 fake AIs whose team pretends to be him every day, promoting fake Bitcoin giveaways via QR codes, and highlighting how persistent these attacks are on social platforms.
Bitget CEO Gracy Chen said, “The speed at which scammers can produce synthetic videos combined with the virality of social media gives them a unique advantage in both reach and faithfulness.”
Related: How fake news and deepfakes drive the latest crypto pump and dump scams
If traditional detection tools are only 69% accurate with real-world deepfakes, they create huge blind spots that criminals exploit. Openai CEO Sam Altman recently warned about the “immediate fraud crisis” as AI “breaks most authentication methods.” The crypto industry needs solutions that evolve as quickly as the threat itself.
These vulnerabilities range from emotional manipulation, just like deepfakes and chatbots create personal relationships and extract funds.
The fundamental problem lies in self-regulating their own production in the midst of political and economic pressures, trusting major AI companies. Google’s SynthID detects only content from its own Gemini system, ignoring deepfakes from competing tools. A conflict of interest is inevitable when the same company that creates generative AI controls the detection system.
A March 2025 survey found that even the best centralized detectors had dropped from 86% accuracy in the controlled dataset to just 69% of actual content. These static systems are expected to train once on existing databases and work forever, but criminals adapt faster than centralized authorities can handle.
Defense from a decentralized code
Decentralized detection networks represent true blockchain principles that apply to digital security. Just as Bitcoin solved the double spending problem by distributing trust, decentralized detection solves the reliability problem by distributing validation to competing miners.
The platform can enable this approach by creating competing incentive mechanisms for AI developers to build good detection models.
Crypto-economy rewards are compensated based on the actual performance of the model against real-world deepfakes, automatically directing talent to the most effective solutions. This competitive framework demonstrates significantly higher accuracy for diverse content compared to centralized alternatives, achieving results that static systems cannot match.
Generated AI will be at a $1.3 trillion market by 2032, so a distributed verification approach will become essential, and a scalable authentication mechanism will be needed to match the rapid development of AI.
Traditional methods are easily altered or bypassed, but centralized databases tend to hack. Only the immutable ledgers of blockchain provide a transparent and secure foundation to combat the surges expected in AI-driven crypto fraud.
Deepfark fraud could represent 70% of crypto crimes without decentralized detection protocols by 2026. Attacks like the $11 million OKX account drain through AI spoofing indicate that intensive exchanges remain vulnerable to sophisticated deepfake attacks.
Pseudonymous transactions already complicate verification, so the Defi platform faces certain risks.
If criminals generate compelling AI identities for the KYC process or pretend to be protocol developers, traditional security measures are found to be inadequate. Distributed detection offers the only scalable solution that matches Defi’s unreliable principles.
Regulation alignment and path to advancement
Regulators increasingly demand robust authentication mechanisms from the Crypto platform, providing consumer tools that decentralized discovery networks already validate content. Why not work with companies that offer auditable, transparent verification that even meets regulatory requirements while maintaining unauthorized innovations that promote blockchain adoption?
The blockchain and cryptocurrency sector are facing a critical time. Stick to centralized detection systems that inevitably employ criminal ingenuity, or use a distributed architecture that transforms industry competitive incentives into a powerful shield against AI fuel fraud.
Opinion: Ken Miyachi, founder of Bitmind.
This article is for general informational purposes and is not intended to be considered legal or investment advice, and should not be done. The views, thoughts and opinions expressed here are the authors alone and do not necessarily reflect or express Cointregraph’s views and opinions.