Papers by Lewis Griffin
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification (2021.emnlp-main)
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| Challenge: | Recent work has raised the question of whether valid adversarial inputs are feasible. |
| Approach: | They analyze how human-generated adversarial examples compare to the best algorithms . they use crowdsourcing to modify words in an input text with immediate feedback . |
| Outcome: | The proposed algorithms are not more efficient than the best to generate natural-reading, sentiment-preserving examples. |
Identifying Human Strategies for Generating Word-Level Adversarial Examples (2022.findings-emnlp)
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| Challenge: | a recent study shows that word-level adversarial examples are more natural and grammatical correct than automated attacks. |
| Approach: | They analyze how humans generate word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammatical correctness. |
| Outcome: | The authors show that humans generate adversarial examples much more effortlessly than automated attacks. |
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples (2021.eacl-main)
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| Challenge: | Existing methods to detect adversarial examples are limited by the nature of these examples. |
| Approach: | They propose a frequency-guided word substitution algorithm that exploits adversarial word substitutions for the detection of adversarials. |
| Outcome: | The proposed algorithm outperforms existing detection methods by 13.0% on the SST-2 and IMDb sentiment datasets. |