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Juan Wen

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AAAI Conference 2026 Conference Paper

BeDKD: Backdoor Defense Based on Directional Mapping Module and Adversarial Knowledge Distillation

Zhengxian Wu
Juan Wen
Wanli Peng
Yinghan Zhou
Changtong Dou
Yiming Xue

Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of clean data to weaken the backdoor mapping but generally struggle with residual trigger effects, resulting in persistently high attack success rates (ASR). Therefore, in this paper, we propose a novel Backdoor defense method based on Directional mapping module and adversarial Knowledge Distillation (BeDKD), which balances the trade-off between defense effectiveness and model performance using a small amount of clean and poisoned data. We first introduce a directional mapping module to identify poisoned data, which destroys clean mapping while keeping backdoor mapping on a small set of flipped clean data. Then, the adversarial knowledge distillation is designed to reinforce clean mapping and suppress backdoor mapping through a cycle iteration mechanism between trust and punish distillations using clean and identified poisoned data. We conduct experiments to mitigate mainstream attacks on three datasets, and experimental results demonstrate that BeDKD surpasses the state-of-the-art defenses and reduces the ASR by 98% without significantly reducing the CACC.

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ECAI Conference 2024 Conference Paper

Domain Adaptational Steganographic Text Detection Using Few-Shot Adversary-Refinement Framework

Ziwei Zhang 0002
Juan Wen
Yinghan Zhou
Liting Gao
Yiming Xue

Text steganography involves discreetly concealing sensitive messages within natural text, while text steganalysis serves as its counterpart by aiming to detect suspicious text that may contain embedded secret information. Detecting steganographic text has become increasingly difficult because evolving steganographic algorithms produce ever-changing text distributions. Consequently, few-shot text steganalysis, which identifies steganographic text with scarce examples regardless of its distribution has become a research hotspot. The state-of-the-art few-shot text steganalysis relies on the inter-class variance between classes, i. e. , they behave satisfactorily in detecting large-variance classes while being incompetent in distinguishing confusable samples from similar steganographic settings. In this paper, we propose an Adversary-Refinement Framework for Text Steganalysis, namely ARTS, which employs a task-invariant extractor and a task-relevant projector to implement an “attract and repel” process. Specifically, in the “attract” stage, we align task-invariant features through adversarial training to shorten the intra-class distance. Afterward, the refined prototypes are projected to a new space in the “repel” stage, and then a refined penalty item is applied to enlarge the inter-class distance. Extensive experiments conducted in six datasets with different inter-class variances demonstrate the superiority of the proposed model over the SOTA models.

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