Camouflage is all you need：评估并增强语言模型对Camouflage对抗攻击的鲁棒性
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks
AI总结：
本研究系统评估了Transformer模型对camouflage对抗攻击的漏洞，并提出整合预伪装与动态修改数据的对抗训练方法，显著增强了模型鲁棒性并开源了相应工具。
摘要：
对抗攻击是自然语言处理（NLP）领域的一项重大挑战。本研究分两个不同阶段对该挑战进行了系统性探索：对抗攻击下Transformer模型漏洞评估与鲁棒性增强。在评估阶段，我们在包含攻击性语言和虚假信息的数据集上，评估了三种Transformer架构（encoder-decoder、encoder-only和decoder-only）面对复杂度不断升级的对抗攻击时的易感性。在攻击性语言检测和虚假信息检测任务中，encoder-only模型的性能分别下降了14%和21%。decoder-only模型在这两项任务中均下降16%，而encoder-decoder模型在相应任务中的最大性能下降幅度分别为14%和26%。鲁棒性增强阶段采用对抗训练，整合了预伪装数据和动态修改数据。该方法有效将encoder-only模型在攻击性语言检测和虚假信息检测任务中的性能下降幅度分别降低至平均5%和2%。decoder-only模型偶尔超越原始性能，将两项任务的性能下降幅度限制在7%和2%。尽管未超越原始性能，encoder-decoder模型也能将下降幅度分别降低至平均6%和2%。结果表明性能与鲁棒性之间存在权衡，部分模型在获得鲁棒性的同时保持了相似性能。我们的研究和对抗训练技术已被整合到一个用于生成camouflage数据集的开源工具中。然而，该方法的有效性取决于所遇到的具体camouflage技术和数据，强调需持续探索。
英文摘要：
Adversarial attacks represent a substantial challenge in Natural Language Processing (NLP). This study undertakes a systematic exploration of this challenge in two distinct phases: vulnerability evaluation and resilience enhancement of Transformer-based models under adversarial attacks. In the evaluation phase, we assess the susceptibility of three Transformer configurations, encoder-decoder, encoder-only, and decoder-only setups, to adversarial attacks of escalating complexity across datasets containing offensive language and misinformation. Encoder-only models manifest a 14% and 21% performance drop in offensive language detection and misinformation detection tasks, respectively. Decoder-only models register a 16% decrease in both tasks, while encoder-decoder models exhibit a maximum performance drop of 14% and 26% in the respective tasks. The resilience-enhancement phase employs adversarial training, integrating pre-camouflaged and dynamically altered data. This approach effectively reduces the performance drop in encoder-only models to an average of 5% in offensive language detection and 2% in misinformation detection tasks. Decoder-only models, occasionally exceeding original performance, limit the performance drop to 7% and 2% in the respective tasks. Although not surpassing the original performance, Encoder-decoder models can reduce the drop to an average of 6% and 2% respectively. Results suggest a trade-off between performance and robustness, with some models maintaining similar performance while gaining robustness. Our study and adversarial training techniques have been incorporated into an open-source tool for generating camouflaged datasets. However, methodology effectiveness depends on the specific camouflage technique and data encountered, emphasizing the need for continued exploration.
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