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.

翻译：人工智能的快速发展使得合成图像的生成变得广泛可及，加剧了大规模在线平台上与虚假信息、数字伪造和内容真实性相关的担忧。本文提出了一种基于区块链的框架，通过注册式溯源机制验证AI生成图像。每个AI生成图像被分配一个数字指纹，该指纹通过感知哈希保持相似性，并由参与生成的平台在创建时进行注册。哈希值使用默克尔帕特里夏树存储于混合链上/链下公共区块链，以实现防篡改存储（链上），并采用Burkhard-Keller树（链下）支持大规模图像注册库的高效相似性搜索。当图像被重新上传至社交媒体等数字平台时进行验证，即使经过良性变换或部分修改，仍能识别先前注册的AI生成图像。本系统不旨在普遍检测所有合成图像，而是专注于验证创建时已注册的AI生成内容的来源。通过设计，该方法与现有水印和基于学习的检测技术形成互补，为大规模在线分发场景提供了平台无关、防篡改的可扩展内容溯源与真实性验证机制。