Papers by Songzhu Zheng
Attention-Enhancing Backdoor Attacks Against BERT-based Models (2023.findings-emnlp)
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| Challenge: | Existing textual backdoor attacks focus on generating stealthy triggers or modifying model weights. |
| Approach: | They propose a Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating attention patterns. |
| Outcome: | The proposed method improves the effectiveness of the backdoor attacks on different backbone models and tasks. |
Task-Agnostic Detector for Insertion-Based Backdoor Attacks (2024.findings-naacl)
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| Challenge: | Existing methods for textual backdoor detection are task-specific and less effective beyond sentence classification. |
| Approach: | They propose a task-agnostic method for backdoor detection that leverages final layer logits and an efficient pooling technique. |
| Outcome: | TABDet can jointly learn from diverse task-specific models, demonstrating superior detection efficacy over traditional methods. |
A Study of the Attention Abnormality in Trojaned BERTs (2022.naacl-main)
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| Challenge: | In computer vision, the trigger can be a fixed pattern overlaid on the images or videos. |
| Approach: | They propose an attention-based Trojan detector to distinguish Trojaned models from clean ones by observing the attention focus drifting behavior of Trojanes. |
| Outcome: | The proposed detector is based on transformer’s attention and can distinguish Trojan models from clean ones. |