11 citations · 12 across the 6 of their papers we have counts for
6 papers
The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks
Zhou Feng, Jiahao Chen, Chunyi Zhou +5
Deep neural networks (DNNs) underpin critical applications yet remain vulnerable to backdoor attacks, typically reliant on heuristic brute-force methods. Despite significant empiri…
CAMH: Advancing Model Hijacking Attack in Machine Learning
Xing He, Jiahao Chen, Yuwen Pu +5
In the burgeoning domain of machine learning, the reliance on third-party services for model training and the adoption of pre-trained models have surged. However, this reliance int…
Enhancing Adversarial Transferability with Adversarial Weight Tuning
Jiahao Chen, Zhou Feng, Rui Zeng +6
Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that mislead the model while appearing benign to human observers. A critical concern is the transferability…
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning
Haozhi Gao, Qianqian Ren, Jinbao Li
Contrastive representation learning is crucial in time series analysis as it alleviates the issue of data noise and incompleteness as well as sparsity of supervision signal. Howeve…
MEAOD: Model Extraction Attack against Object Detectors
Zeyu Li, Chenghui Shi, Yuwen Pu +4
The widespread use of deep learning technology across various industries has made deep neural network models highly valuable and, as a result, attractive targets for potential atta…
Attentive Graph Enhanced Region Representation Learning
Weiliang Chen, Qianqian Ren, Jinbao Li
Representing urban regions accurately and comprehensively is essential for various urban planning and analysis tasks. Recently, with the expansion of the city, modeling long-range…