Papers by Benjamin Rubinstein
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation (2023.emnlp-main)
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| Challenge: | Modern NLP models are often trained over large untrustworthy datasets, raising the potential for a malicious adversary to compromise model behaviour. |
| Approach: | They propose to mitigate spurious correlations between textual triggers and classification labels by combining them with insertion-based attacks. |
| Outcome: | The proposed defence significantly reduces attack success rates across backdoor attacks and provides a near-perfect defence against insertion-based attacks. |
Measuring and Mitigating Name Biases in Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural machine translation systems exhibit problematic biases, such as stereotypical gender bias in occupation terms. |
| Approach: | They propose a method to reduce biases in person name translations by randomly switching entities during translation. |
| Outcome: | The proposed method eliminates the problem without any effect on translation quality. |
Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning (2021.findings-acl)
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Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin Rubinstein, Trevor Cohn
| Challenge: | Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, they are also vulnerable to training attacks. |
| Approach: | They propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into a training set of a system trained using back-translation. |
| Outcome: | The proposed attack is based on two methods that can be used to craft poisoned examples. |
Foiling Training-Time Attacks on Neural Machine Translation Systems (2022.findings-emnlp)
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| Challenge: | Neural machine translation systems are vulnerable to backdoor attacks . successful backdoors can cause slander, hate speech, phishing, etc. attacks can target very short trigger phrases, which can be challenging to detect even when included verbatim in poisoned instances. |
| Approach: | They propose a method that exploits asymmetry between source and target sentences to detect outlier tokens. |
| Outcome: | The proposed method reduces the success of attacks by up to 89.0% while not affecting predictive accuracy. |
Backdoor Attacks on Multilingual Machine Translation (2024.naacl-long)
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| Challenge: | Recent studies have shown that multilingual machine translation systems are vulnerable to backdoor attacks through data poisoning. |
| Approach: | They propose to investigate the security of multilingual machine translation systems by exposing poisoned data into low-resource languages to cause malicious translations. |
| Outcome: | The proposed method achieves an average of 20% success rate in attacking high-resource languages. |
Mitigating Data Poisoning in Text Classification with Differential Privacy (2021.findings-emnlp)
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| Challenge: | Data poisoning attacks can plant a backdoor in a model by injecting poisoned examples into training data, causing the model to misclassify test instances which include a specific pattern. |
| Approach: | They propose a generic defence mechanism that makes training robust to poisoning attacks by smoothing the gradient from each training example. |
| Outcome: | The proposed method is highly effective in mitigating, or even eliminating, poisoning attacks on text classification, with only a small cost in predictive accuracy. |
CERT-ED: Certifiably Robust Text Classification for Edit Distance (2024.findings-emnlp)
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| Challenge: | randomized smoothing has emerged as promising approach for certifying robustness to adversarial examples. |
| Approach: | They propose a method for certifying robustness to adversarial examples . they adapt Randomized Deletion and propose CERTified Edit Distance defense . |
| Outcome: | The proposed method outperforms existing methods in 4 out of 5 datasets in accuracy and cardinality. |
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation (2021.findings-acl)
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| Challenge: | Mistranslated numbers can cause financial loss or medical misinformation. |
| Approach: | They propose a method to assess the robustness of neural machine translation systems to numerical text via behavioural testing. |
| Outcome: | The proposed method systematically assesses four fundamental capabilities of neural machine translation systems in translation numbers by virtue of a variety of test cases. |