Summary of Forging the Forger: An Attempt to Improve Authorship Verification Via Data Augmentation, by Silvia Corbara and Alejandro Moreo
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
by Silvia Corbara, Alejandro Moreo
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper investigates ways to improve Authorship Verification (AV) systems’ robustness against adversarial attacks. AV aims to identify whether a given text was written by a specific author or not. Current AV systems can be easily fooled by malicious authors who attempt to hide their writing style or imitate another author’s style. To address this issue, the researchers propose augmenting the classifier training set with synthetic examples generated to mimic the target author’s style. They explore three different generator architectures and two training strategies to evaluate the effectiveness of this approach in an adversarial setting. The study uses five datasets and two learning algorithms for the AV classifier, but unfortunately, the results show that the benefits are too sporadic for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make better computers that can figure out who wrote a piece of writing. They want to make sure these computers aren’t tricked by people trying to hide or copy someone else’s style. One way they do this is by creating fake examples that mimic the real author’s style, and then training the computer on those examples. The researchers try three different ways to create these fake examples and two different methods for training the computer. They test their ideas using five sets of writings and two types of learning algorithms. Unfortunately, their results show that this approach isn’t reliable enough to be used in real-life situations. |
