"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks

• URL: http://arxiv.org/abs/2204.04636v2
• Date: Thu, 29 Jun 2023 13:02:28 GMT
• Title: "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks
• Authors: Edoardo Mosca and Shreyash Agarwal and Javier Rando and Georg Groh
• Abstract summary: Adversarial attacks are a major challenge faced by current machine learning research. Our work presents a model-agnostic detector of adversarial text examples.
• Score: 0.2999888908665659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, the same issue remains less explored in natural language processing. Our work presents a model-agnostic detector of adversarial text examples. The approach identifies patterns in the logits of the target classifier when perturbing the input text. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks.

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