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

Edoardo Mosca; Shreyash Agarwal; Javier Rando; Georg Groh

doi:10.18653/v1/2022.acl-long.538

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arXiv · 2204.04636

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

Edoardo Mosca·Shreyash Agarwal·Javier Rando·Georg Groh

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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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Edoardo Mosca, Shreyash Agarwal, Javier Rando, Georg Groh. 2022-04-10. "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks. https://doi.org/10.18653/v1%2F2022.acl-long.538

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