Presentation
QMark: Robust Watermarks for IP Protection of Quantized Large Language Models
DescriptionThis paper introduces EmMark, a novel watermarking framework for protecting intellectual property (IP) of embedded large language models deployed on resource-constrained edge devices. To address the IP theft risks posed by malicious end-users, EmMark enables proprietors to authenticate ownership by querying the watermarked model weights and matching the inserted signatures. EmMark's novelty lies in its strategic watermark weight parameters selection, ensuring robustness and maintaining model quality.
Extensive proof-of-concept evaluations of models from OPT and LLaMA-2 families demonstrate EmMark's fidelity, achieving 100% success in watermark extraction with model performance preservation. EmMark also showcased its resilience against watermark removal and forging attacks.
Extensive proof-of-concept evaluations of models from OPT and LLaMA-2 families demonstrate EmMark's fidelity, achieving 100% success in watermark extraction with model performance preservation. EmMark also showcased its resilience against watermark removal and forging attacks.
Event Type
Research Manuscript
TimeWednesday, June 264:00pm - 4:15pm PDT
Location3002, 3rd Floor
AI
Security
AI/ML Security/Privacy