VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations (2023.findings-acl)
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| Challenge: | Existing methods for detecting adversarial attacks need original/adversarial data. |
| Approach: | They propose to detect adversarial text by voting on hard labels from predictions of transformations using a model that uses original/adversarial data. |
| Outcome: | The proposed method detects adversarial text across various state-of-the-art attacks, models, and datasets without training. |
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| Challenge: | Existing work detects adversarial text and corrects the prediction by identifying perturbed words and changing them into their synonyms, but many benign words are also changed. |
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| Challenge: | Recent studies have pointed out the vulnerability of deep learning models to adversarial attacks. |
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| Challenge: | Existing methods for obtaining adversarial examples are difficult with text data. |
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| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
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| Challenge: | Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications. |
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| Challenge: | Existing text adversarial attacks are impractical in real-world scenarios where humans are involved. |
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
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Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning (P18-1)
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| Challenge: | Visual language grounding is widely studied in modern neural image captioning systems . a novel algorithm for crafting adversarial examples in image captions is proposed . |
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