Related papers: Provenance Verification of AI-Generated Images via a Perceptual Hash Registry Anchored on Blockchain

Provenance Verification of AI-Generated Images via a Perceptual Hash Registry Anchored on Blockchain

URL: http://arxiv.org/abs/2602.02412v1
Date: Mon, 02 Feb 2026 18:13:09 GMT
Title: Provenance Verification of AI-Generated Images via a Perceptual Hash Registry Anchored on Blockchain
Authors: Apoorv Mohit, Bhavya Aggarwal, Chinmay Gondhalekar,
Abstract summary: This paper proposes a blockchain-backed framework for verifying AI-generated images through a registry-based provenance mechanism.<n>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.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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.

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