36 citations · 162 across the 17 of their papers we have counts for
12 papers · 1 filter
BackdoorBox: A Python Toolbox for Backdoor Learning
Yiming Li, Mengxi Ya, Yang Bai +2
Third-party resources (, samples, backbones, and pre-trained models) are usually involved in the training of deep neural networks (DNNs), which brings backdoor attacks as a n…
BATT: Backdoor Attack with Transformation-based Triggers
Tong Xu, Yiming Li, Yong Jiang +1
Deep neural networks (DNNs) are vulnerable to backdoor attacks. The backdoor adversaries intend to maliciously control the predictions of attacked DNNs by injecting hidden backdoor…
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
Yiming Li, Yang Bai, Yong Jiang +3
Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets,…
Black-box Dataset Ownership Verification via Backdoor Watermarking
Yiming Li, Mingyan Zhu, Xue Yang +3
Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid d…
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
Yiming Li, Linghui Zhu, Xiaojun Jia +5
Currently, deep neural networks (DNNs) are widely adopted in different applications. Despite its commercial values, training a well-performing DNN is resource-consuming. Accordingl…
Defending against Model Stealing via Verifying Embedded External Features
Yiming Li, Linghui Zhu, Xiaojun Jia +3
Obtaining a well-trained model involves expensive data collection and training procedures, therefore the model is a valuable intellectual property. Recent studies revealed that adv…