Summary of Cap: Detecting Unauthorized Data Usage in Generative Models Via Prompt Generation, by Daniela Gallo et al.
CAP: Detecting Unauthorized Data Usage in Generative Models via Prompt Generation
by Daniela Gallo, Angelica Liguori, Ettore Ritacco, Luca Caviglione, Fabrizio Durante, Giuseppe Manco
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Copyright Audit via Prompts generation (CAP) framework aims to automatically test whether a machine learning model has been trained with unauthorized data. To achieve accurate and unbiased predictions, ML models rely on large, heterogeneous, and high-quality datasets. However, this could raise ethical and legal concerns regarding copyright and authorization aspects, especially when information is gathered from the Internet. The CAP framework generates suitable keys inducing the model to reveal copyrighted contents. An extensive evaluation campaign was conducted on measurements collected in four IoT scenarios, showcasing the effectiveness of CAP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep machine learning models honest by making sure they aren’t using stolen data. It does this with a special tool called Copyright Audit via Prompts generation (CAP). CAP looks for signs that a model has been trained on copyrighted material. This is important because big language models can easily copy and paste things from the internet, which is bad news if you’re trying to protect someone’s work. |
Keywords
* Artificial intelligence * Machine learning
