Papers by Zubair Shafiq
A Girl Has A Name: Detecting Authorship Obfuscation (2020.acl-main)
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| Challenge: | Existing authorship attribution methods are not stealthy as they degrade text smoothness in detectable manner. |
| Approach: | They evaluate the stealthiness of authorship attribution methods under an adversarial threat model and show that they are not stealthy . |
| Outcome: | The proposed methods can be identified with an average F1 score of 0.87 . |
On the Robustness of Offensive Language Classifiers (2022.acl-long)
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| Challenge: | Existing studies on offensive language classifiers have focused on primitive attacks such as misspellings and extraneous spaces. |
| Approach: | They analyze the robustness of offensive language classifiers against crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
| Outcome: | The proposed classifiers are robust against more crafty attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
Fingerprinting Fine-tuned Language Models in the Wild (2021.findings-acl)
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| Challenge: | Existing fingerprinting methods to fingerprint language models are limited to attributing organic text . however, fine-tuned LMs can generate long, coherent, and grammatically valid synthetic text. |
| Approach: | They conduct extensive experiments to demonstrate the limitations of existing fingerprinting approaches. |
| Outcome: | The proposed fingerprinting methods are limited to attributing synthetic text generated by 10 pre-trained LMs. |
Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models (2021.eacl-main)
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| Challenge: | Recent advances in natural language processing have enabled synthetic text generation that is often comparable to the organic text. |
| Approach: | They propose and test several ML-based methods to attribute authorship of synthetic text to language models (LMs) they propose to use a fine-tuned version of XLNet to achieve excellent accuracy . |
| Outcome: | The proposed method achieves excellent accuracy (91% to near perfect 98%) across a range of experiments where the synthetic text may be generated using pre-trained LMs, fine-tuned LM, or by varying text generation parameters. |
Adversarial Authorship Attribution for Deobfuscation (2022.acl-long)
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| Challenge: | Existing authorship attribution approaches do not consider adversarial threat model . authors show adversarially trained authorship attributors can degrade effectiveness of existing obfuscators from 20-30% to 5-10% . |
| Approach: | They propose to use rule-based and learning-based text obfuscation approaches to counter authorship attribution. |
| Outcome: | The proposed approaches do not consider the adversarial threat model . authors show that adversarially trained attributors can degrade effectiveness of existing obfuscators from 20-30% to 5-10% . |