Authors: Aditya Desu, Xuanli He, Qiongkai Xu, Wei Lu

Published on: February 23, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2402.16889

Summary

• What is new: A novel approach to verify data ownership that leverages latent fingerprints in the outputs of generative models, bypassing the need for traditional watermarking.
• Why this is important: Challenges in protecting the intellectual property of generative models, especially with the rise of Machine Learning as a Service (MLaaS) and unauthorized data reuse.
• What the research proposes: An explainable verification technique that attributes data ownership through re-generation, amplifying inherent latent fingerprints without compromising output quality.
• Results: Demonstrated viability and robustness of the method across recent text and image generative models, highlighting its potential to safeguard against misinformation and academic misconduct.

Technical Details

Technological frameworks used: Explainable AI procedures

Models used: Advanced text and image generative models

Data used: Iterative data re-generation

Potential Impact

Machine Learning as a Service (MLaaS) providers, educational platforms, news agencies, and IP-centric businesses.

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