Papers with attack models
OpenAttack: An Open-source Textual Adversarial Attack Toolkit (2021.acl-demo)
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Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun
| Challenge: | Various attack models are distinct and implemented with different programming frameworks and settings, which hinders quick utilization and fair comparison of attack models. |
| Approach: | They propose an open-source textual adversarial attack toolkit to solve these issues by combining 15 typical attack models into one toolkit. |
| Outcome: | The proposed toolkit supports all attack types, multilinguality, and parallel processing. |
Multi-granularity Textual Adversarial Attack with Behavior Cloning (2021.emnlp-main)
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| Challenge: | Existing adversarial attack models are vulnerable to adversarials crafted by human-imperceptible perturbations. |
| Approach: | They propose a multi-granularity adversarial attack model that generates high-quality adversarials with fewer queries to victim models. |
| Outcome: | The proposed model generates high-quality adversarial samples with fewer queries to victim models compared to baseline models . the proposed model also reduces query times for black-box models that only output labels without confidence scores . |
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic (2023.acl-long)
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| Challenge: | Despite the recent advances in distributed representation and neural networks, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. |
| Approach: | They propose to use logic formalism to perform systematic attacks centring around natural logic to generate better adversarial examples with fewer visits to the victim models. |
| Outcome: | The proposed framework generates better adversarial examples with fewer visits to the victim models. |