With the rapid development of AI deepfake technology, false content is emerging endlessly, threatening the authenticity of information and the foundation of trust. The decentralized and traceable features of Web3 provide new ideas for combating counterfeiting. This article is aimed at newcomers, using simple metaphors, case studies, and data to analyze the anti-counterfeiting application scenarios and practical suggestions of blockchain in the AI era. "Follow for more updates"

🔹 Why is blockchain needed?

  • Decentralized Verification: It is difficult to distinguish authenticity in AI-generated content; blockchain can store 'content fingerprints' or signatures, allowing recipients to verify authenticity.

  • Ownership and Copyright Protection: If works created by AI are put on-chain, it can record the source of creators or model training to prevent theft or tampering.

  • Traceability: The immutable nature on-chain records the generation and flow path of content, allowing for source tracing in case of disputes.

  • Incentive Mechanism: Incentivize community participation in verifying, marking, or reporting false content through tokens, forming a collaborative anti-counterfeiting network.

🔸 Simple Metaphors

  • Digital Fingerprint: A unique hash is automatically generated for each piece of text and each image, and once on-chain, it’s like affixing an anti-counterfeiting label to the content; any alteration will result in a change of the hash, making it detectable.

  • Anti-counterfeiting Label + Public Ledger: The anti-counterfeiting QR code on product packaging is recorded in a public ledger, allowing anyone to verify authenticity by scanning; AI content can also be verified for source and integrity in the same way.

▶️ Typical Application Scenarios and Cases

  • Media Verification: Before releasing important news or videos, media outlets hash the content on-chain, allowing readers or platforms to verify that the content has not been tampered with.

  • Social Platform Anti-Counterfeiting: When users upload photos/videos, the client automatically generates a hash and puts it on-chain, ensuring that any reposting or modification can be detected.

  • AI Model Traceability: In distributed AI training or model markets, use blockchain to record model versions and contributors to ensure intellectual property.

  • NFT and AI Art: AI-generated works are minted as NFTs, recording generation parameters and creator identities to ensure trustworthy sources.

  • Decentralized Identity (DID) Integration: Verify the identity of publishers through DID, then combine content signatures and on-chain verification to enhance credibility.

  • Practical Projects:

    • Truepic + Blockchain: Use trusted hardware to take photos and put metadata on-chain to prevent counterfeiting.

    • Po.et: Record publication metadata and hashes on the blockchain to verify article originality.

    • Content Authentication DApps: Store file hashes on IPFS + blockchain to provide verification services.

  • Risk Scale: Research shows that about 30% of online information carries a counterfeiting risk, and blockchain verification can improve information credibility by about 40% (example data, see industry reports for specifics).

  • Market Attention: There is an increasing discussion on relevant anti-counterfeiting projects and standards, with several media outlets and technology summits specifically exploring AI + blockchain anti-counterfeiting solutions.

  • Technological Progress: The cost of blockchain storage has decreased (e.g., using IPFS/Arweave for hash storage), and the logic for automatic verification using smart contracts has become more mature.

🔧 Newcomer Practice Guide

  • Experience Hash On-Chain: Use a test network to generate a hash from a piece of text or image and write it into a simple contract to experience the verification process.

  • Focus on Open Source Projects: Browse anti-counterfeiting examples or DApps on GitHub to understand the basic ideas.

  • Use Social Anti-Counterfeiting Tools: Try using social platforms or plugins with signature and on-chain verification features to experience the trustworthy publishing process.

  • Participate in Community Discussions: Follow relevant forums, Telegram, and Discord groups to learn about the latest projects and standard developments.

  • Consider Incentive Mechanisms: Explore how to incentivize users to participate in content verification and flagging false information through token or NFT mechanisms, forming a decentralized anti-counterfeiting network.

💡 It is important to understand that:

  • AI deepfakes pose challenges to information security, but the decentralized, traceable, and tamper-proof features of blockchain provide technical support for 'anti-counterfeiting labels.'

  • Newcomers can start practicing by generating and verifying content hashes, focusing on existing projects and standards to understand how the ecosystem collaborates to combat counterfeiting.

  • The combination of AI + Web3 is not only a technological innovation but also an important direction for building a digital trust system.