通过区块链锚定的感知哈希注册表验证AI生成图像的溯源
Provenance Verification of AI-Generated Images via a Perceptual Hash Registry Anchored on Blockchain
AI总结：
本文提出一种基于区块链的感知哈希注册表机制，用于验证AI生成图像的溯源，通过混合链上链下存储实现防篡改和高效相似性搜索。
摘要：
人工智能的快速发展使合成图像的生成变得广泛普及，增加了大型在线平台上虚假信息、数字伪造和内容真实性的问题。本文提出了一种基于区块链的框架，通过基于注册表的溯源机制来验证AI生成图像。每个AI生成的图像都会被分配一个数字指纹，利用感知哈希保持相似性，并在生成时由参与的生成平台注册。这些哈希值通过混合的链上/链下公共区块链进行存储，使用Merkle Patricia Trie进行防篡改存储（链上）和Burkhard-Keller树（链下）以实现对大规模图像注册表的高效相似性搜索。当图像重新上传到数字平台如社交媒体服务时进行验证，能够识别已注册的AI生成图像，即使经过良性变换或部分修改。所提出系统的目标不是普遍检测所有合成图像，而是专注于验证在生成时已注册的AI生成内容的溯源。通过设计，这种方法补充了现有水印和基于学习的检测方法，提供了一种平台无关、防篡改的机制，用于大规模在线分发中的可扩展内容溯源和真实性验证。
英文摘要：
The rapid advancement of artificial intelligence has made the generation of synthetic images widely accessible, increasing concerns related to misinformation, digital forgery, and content authenticity on large-scale online platforms. This paper proposes a blockchain-backed framework for verifying AI-generated images through a registry-based provenance mechanism. Each AI-generated image is assigned a digital fingerprint that preserves similarity using perceptual hashing and is registered at creation time by participating generation platforms. The hashes are stored on a hybrid on-chain/off-chain public blockchain using a Merkle Patricia Trie for tamper-resistant storage (on-chain) and a Burkhard-Keller tree (off-chain) to enable efficient similarity search over large image registries. Verification is performed when images are re-uploaded to digital platforms such as social media services, enabling identification of previously registered AI-generated images even after benign transformations or partial modifications. The proposed system does not aim to universally detect all synthetic images, but instead focuses on verifying the provenance of AI-generated content that has been registered at creation time. By design, this approach complements existing watermarking and learning-based detection methods, providing a platform-agnostic, tamper-proof mechanism for scalable content provenance and authenticity verification at the point of large-scale online distribution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
