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Results for "Watermarking"

Challenging the promise of invisible ink in the era of large models

MBZUAI · Invalid Date

MBZUAI researchers Nils Lukas and Toluwani Samuel Aremu will present a paper at ICML 2025 demonstrating the vulnerability of current watermarking techniques in LLMs. Their research shows that adaptive paraphrasers can evade detection from watermarks with negligible impact on text quality, costing less than $10 of GPU compute. The attack involves fine-tuning a small open-weight model to rewrite sentences until surrogate keys no longer trigger detection. Why it matters: This work highlights critical weaknesses in current AI provenance methods, suggesting the need for more robust watermarking techniques to maintain trust in the authenticity of AI-generated content.

Analyzing Threats of Large-Scale Machine Learning Systems

MBZUAI · Invalid Date

A PhD candidate from the University of Waterloo presented on threats from large machine learning systems at MBZUAI. The talk covered data privacy during inference and the misuse of ML systems to generate deepfakes. The speaker also analyzed differential privacy and watermarking as potential solutions. Why it matters: Understanding and mitigating the risks of large ML systems is crucial for responsible AI development and deployment in the region.