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Register a new userSearching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
البحث عن مدافع فعال: معيار الدفاع ضد استبدال كلمة العدسات
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أظهرت الدراسات الحديثة أن النماذج العميقة العصبية المستندة إلى الشبكة المعرضة للأمثلة المصنوعة عن قصد، ويقترح أساليب مختلفة للدفاع ضد هجمات استبدال الكلمات العدائية لنماذج NLP العصبية. ومع ذلك، هناك نقص في الدراسة المنهجية حول مقارنة النهج الدفاعية المختلفة بموجب نفس الإعداد الهجومية. في هذه الورقة، نسعى إلى ملء فجوة الدراسات المنهجية من خلال أبحاث شاملة بشأن فهم سلوك مصنفات النص العصبي المدربين من قبل طرق دفاعية مختلفة بموجب هجمات المشدلات التمثيلية. بالإضافة إلى ذلك، نقترح طريقة فعالة لزيادة تحسين متانة المصنفات النصية العصبية ضد هذه الهجمات، وحققت أعلى دقة على كل من الأمثلة النظيفة والمنعدة على مجموعات بيانات Agnews و IMDB بمهامش مهم. نأمل أن توفر هذه الدراسة أدلة مفيدة للبحث في المستقبل على الدفاع المشددي النصي. تتوفر الرموز في https://github.com/rockylzy/textdefender.
Recent studies have shown that deep neural network-based models are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting. In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks, and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets by a significant margin. We hope this study could provide useful clues for future research on text adversarial defense. Codes are available at https://github.com/RockyLzy/TextDefender.
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