🤖 AI Summary
Existing image watermarking methods treat payloads as semantically agnostic bitstreams, limiting capacity and precluding embedding of human-interpretable, high-level semantic information.
Method: We propose a novel semantic watermarking paradigm: (i) compress full natural-language sentences into 256-dimensional unit-norm latent vectors via a lightweight text autoencoder; (ii) fine-tune a watermarking model for robust embedding; and (iii) enforce security via a secret, invertible rotation transformation.
Contributions/Results: Our approach breaks the conventional 256-bit capacity ceiling, enabling sentence-level semantic steganography and millisecond-scale real-time decoding. A statistically calibrated scoring mechanism supports AI-generated content provenance tracing and tampering attribution. Evaluated on multiple benchmarks, it substantially outperforms state-of-the-art methods—achieving superior BLEU-4 and Exact Match scores, and ROC AUC of 0.97–0.99—while maintaining strong robustness against geometric and value-domain attacks and offering full interpretability.
📝 Abstract
Most image watermarking systems focus on robustness, capacity, and imperceptibility while treating the embedded payload as meaningless bits. This bit-centric view imposes a hard ceiling on capacity and prevents watermarks from carrying useful information. We propose LatentSeal, which reframes watermarking as semantic communication: a lightweight text autoencoder maps full-sentence messages into a compact 256-dimensional unit-norm latent vector, which is robustly embedded by a finetuned watermark model and secured through a secret, invertible rotation. The resulting system hides full-sentence messages, decodes in real time, and survives valuemetric and geometric attacks. It surpasses prior state of the art in BLEU-4 and Exact Match on several benchmarks, while breaking through the long-standing 256-bit payload ceiling. It also introduces a statistically calibrated score that yields a ROC AUC score of 0.97-0.99, and practical operating points for deployment. By shifting from bit payloads to semantic latent vectors, LatentSeal enables watermarking that is not only robust and high-capacity, but also secure and interpretable, providing a concrete path toward provenance, tamper explanation, and trustworthy AI governance. Models, training and inference code, and data splits will be available upon publication.