Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
158 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Rethinking Targeted Adversarial Attacks For Neural Machine Translation (2407.05319v1)

Published 7 Jul 2024 in cs.CL

Abstract: Targeted adversarial attacks are widely used to evaluate the robustness of neural machine translation systems. Unfortunately, this paper first identifies a critical issue in the existing settings of NMT targeted adversarial attacks, where their attacking results are largely overestimated. To this end, this paper presents a new setting for NMT targeted adversarial attacks that could lead to reliable attacking results. Under the new setting, it then proposes a Targeted Word Gradient adversarial Attack (TWGA) method to craft adversarial examples. Experimental results demonstrate that our proposed setting could provide faithful attacking results for targeted adversarial attacks on NMT systems, and the proposed TWGA method can effectively attack such victim NMT systems. In-depth analyses on a large-scale dataset further illustrate some valuable findings. 1 Our code and data are available at https://github.com/wujunjie1998/TWGA.

Summary

We haven't generated a summary for this paper yet.