Summary of Cert-ed: Certifiably Robust Text Classification For Edit Distance, by Zhuoqun Huang et al.
CERT-ED: Certifiably Robust Text Classification for Edit Distance
by Zhuoqun Huang, Neil G Marchant, Olga Ohrimenko, Benjamin I. P. Rubinstein
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on certifying the robustness of natural language classification systems to inference-time attacks. The authors build upon previous work on randomized smoothing, a promising approach for ensuring robustness. They propose a new defense method, CERTified Edit Distance (CERT-ED), which outperforms existing methods like Hamming distance in four out of five datasets. CERT-ED uses Randomized Deletion, a technique introduced by Huang et al. in 2023. The authors demonstrate the effectiveness of their method through comprehensive experiments, covering various threat models and attack scenarios. The paper contributes to the development of robust AI systems for natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if your phone or computer got hacked at a time when you needed it most! To prevent this from happening, researchers are working on making artificial intelligence (AI) more secure. They’ve developed a new way to protect against attacks that try to trick AI systems. This method, called CERT-ED, is better than what’s currently available and can be used for many different types of language-based tasks. The researchers tested their approach and found it works well in most cases. By making AI more secure, we can all benefit from the many amazing things it can do. |
Keywords
» Artificial intelligence » Classification » Inference » Natural language processing
