🤖 AI Summary
This work proposes a blockchain-based trusted registration and verification mechanism to address the challenges of misinformation and source tracing posed by the proliferation of AI-generated images. At the time of image generation, a perceptual hash fingerprint is assigned and immutably recorded using a hybrid on-chain/off-chain architecture, where the fingerprint is stored in a Merkle Patricia Trie. To enable efficient similarity search despite benign transformations or partial modifications, the system integrates a Burkhard–Keller tree. This approach ensures tamper-resistant, platform-agnostic provenance tracking that remains robust under common image alterations. By overcoming the limitations of conventional watermarking and detection techniques, the proposed framework significantly enhances the ability of large-scale online platforms to verify the authenticity of AI-generated content.
📝 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.