Adversarial Semantic Collisions

Congzheng Song; Alexander M. Rush; Vitaly Shmatikov

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arXiv · 2011.04743

Adversarial Semantic Collisions

Congzheng Song·Alexander M. Rush·Vitaly Shmatikov

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Abstract

We study semantic collisions: texts that are semantically unrelated but judged as similar by NLP models. We develop gradient-based approaches for generating semantic collisions and demonstrate that state-of-the-art models for many tasks which rely on analyzing the meaning and similarity of texts-- including paraphrase identification, document retrieval, response suggestion, and extractive summarization-- are vulnerable to semantic collisions. For example, given a target query, inserting a crafted collision into an irrelevant document can shift its retrieval rank from 1000 to top 3. We show how to generate semantic collisions that evade perplexity-based filtering and discuss other potential mitigations. Our code is available at https://github.com/csong27/collision-bert.

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Congzheng Song, Alexander M. Rush, Vitaly Shmatikov. 2020-11-09. Adversarial Semantic Collisions. https://arxiv.org/abs/2011.04743

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