Summary of Enhancing Adversarial Text Attacks on Bert Models with Projected Gradient Descent, by Hetvi Waghela et al.
Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent
by Hetvi Waghela, Jaydip Sen, Sneha Rakshit
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 proposes an enhancement to the BERT-Attack framework for generating adversarial examples against BERT-based natural language processing (NLP) models. The original BERT-Attack has limitations such as a fixed perturbation budget and neglecting semantic similarity. The proposed PGD-BERT-Attack addresses these issues by using Projected Gradient Descent (PGD) to iteratively generate adversarial examples that are both imperceptible and semantically similar to the input. Experiments demonstrate higher success rates for misclassification while maintaining low perceptual changes, with greater semantic resemblance to the initial input. This enhanced approach contributes to defense against attacks on NLP systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves a way to make artificial intelligence models for language processing fail by adding tiny changes to the text that are hard to notice. The current method has some problems, like not considering how similar the changed text is to the original. The new method uses a special technique called Projected Gradient Descent (PGD) to create these attacks while making sure they’re small and make sense. Tests show this new approach works better at fooling the models without changing the text too much. |
Keywords
* Artificial intelligence * Bert * Gradient descent * Natural language processing * Nlp
