The accelerated evolution of generative AI technologies has led to an exponential increase in synthetic digital content, amplifying concerns related to misinformation, manipulation, and content authenticity. In response to these emerging challenges, this paper introduces a theoretically grounded and multi-layered framework designed to ensure provenance and trust in AI-generated imagery. Our approach leverages a fusion of cryptographic hashing, blockchain technology, steganography, and metadata embedding to enable reliable origin tracing of synthetic media. Inspired by the traceability mechanisms employed in blockchain-backed assets, each AI-generated image is assigned a unique, tamper-evident hash derived from its inherent visual features and user-specific metadata. This dual-layered embedding ensures resilience against metadata stripping and redistribution, offering a foundational infrastructure for digital trust and accountability.
Singh et al. (Tue,) studied this question.
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