Summary of Adversarial Decoding: Generating Readable Documents For Adversarial Objectives, by Collin Zhang et al.
Adversarial Decoding: Generating Readable Documents for Adversarial Objectives
by Collin Zhang, Tingwei Zhang, Vitaly Shmatikov
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 presents a new text generation technique called adversarial decoding, which produces readable documents for various adversarial objectives. Unlike previous methods, this approach can handle embedding similarity and produce adversarial texts for realistic indirect injection scenarios. The authors demonstrate the effectiveness of their method in evading filtering and outperforming existing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to generate text that is good at fooling computer systems. It’s like writing a fake document that looks real, but it’s not just random nonsense. This technique can make documents that get found when you search for something general, or even make documents that try to trick filters designed to catch fake texts. The authors show that this method works better than others and makes readable fake texts. |
Keywords
» Artificial intelligence » Embedding » Text generation
