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Textual Manifold-based Defense Against Natural Language Adversarial Examples. (arXiv:2211.02878v1 [cs.CL])
Nov. 8, 2022, 2:20 a.m. | Dang Minh Nguyen, Luu Anh Tuan
cs.CR updates on arXiv.org arxiv.org
Recent studies on adversarial images have shown that they tend to leave the
underlying low-dimensional data manifold, making them significantly more
challenging for current models to make correct predictions. This so-called
off-manifold conjecture has inspired a novel line of defenses against
adversarial attacks on images. In this study, we find a similar phenomenon
occurs in the contextualized embedding space induced by pretrained language
models, in which adversarial texts tend to have their embeddings diverge from
the manifold of natural ones. …
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