97 citations · 847 across the 68 of their papers we have counts for
10 papers · 1 filter
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy
Zihao Zhu, Mingda Zhang, Shaokui Wei +3
Data-poisoning based backdoor attacks aim to insert backdoor into models by manipulating training datasets without controlling the training process of the target model. Existing at…
Versatile Weight Attack via Flipping Limited Bits
Jiawang Bai, Baoyuan Wu, Zhifeng Li +1
To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the…
Backdoor Defense via Decoupling the Training Process
Kunzhe Huang, Yiming Li, Baoyuan Wu +2
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few trainin…
Hidden Backdoor Attack against Semantic Segmentation Models
Yiming Li, Yanjie Li, Yalei Lv +2
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data. The attacked model behaves nor…
Invisible Backdoor Attack with Sample-Specific Triggers
Yuezun Li, Yiming Li, Baoyuan Wu +3
Recently, backdoor attacks pose a new security threat to the training process of deep neural networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the a…
Open-sourced Dataset Protection via Backdoor Watermarking
Yiming Li, Ziqi Zhang, Jiawang Bai +3
The rapid development of deep learning has benefited from the release of some high-quality open-sourced datasets (, ImageNet), which allows researchers to easily verify the e…
Backdoor Attack against Speaker Verification
Tongqing Zhai, Yiming Li, Ziqi Zhang +3
Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount o…
Backdoor Learning: A Survey
Yiming Li, Yong Jiang, Zhifeng Li +1
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be mal…
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
Yan Feng, Baoyuan Wu, Yanbo Fan +3
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while…
Rethinking the Trigger of Backdoor Attack
Yiming Li, Tongqing Zhai, Baoyuan Wu +3
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden bac…