Statistical Signal Amplification for Watermark Verification in Low-Rank Diffusion Adaptations
As the World Wide Web evolves into the central infrastructure for AI-generated content (AIGC), ensuring the provenance of assets distributed via online platforms has become a critical challenge in Web Engineering. The uncontrolled propagation of Low-Rank Adaptation (LoRA) models facilitates unauthorized style mimicry, yet existing watermarks often fail to survive LoRA’s parameter compression. To safeguard digital trust and creator rights, we propose an Adaptation-Agnostic Trace Verification method optimized for secure web ecosystems. Our approach combines deep learning-based watermarking with a Statistical Resonance Amplifier (SRA) to induce the transfer of high-frequency signals into model weights. Furthermore, to overcome the noise limitations of single-image analysis in distributed web applications, we introduce an ensemble-based detection technique. Experimental results validate the method’s robustness, achieving an AUC-ROC of 0.891 even in highly restricted Rank 32 environments (using 100 generated images) and a near-perfect 0.999 at Rank 128, without degrading generation quality. This study presents a practical technology for AI governance and copyright protection, essential for ensuring the trustworthiness of AI-enhanced Web services.