154 citations · 224 across the 10 of their papers we have counts for
9 papers · 1 filter
Context-Aware Robust Fine-Tuning
Xiaofeng Mao, Yuefeng Chen, Xiaojun Jia +3
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sent…
Boosting Out-of-distribution Detection with Typical Features
Yao Zhu, YueFeng Chen, Chuanlong Xie +6
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD det…
Sharp Multiple Instance Learning for DeepFake Video Detection
Xiaodan Li, Yining Lang, Yuefeng Chen +5
With the rapid development of facial manipulation techniques, face forgery has received considerable attention in multimedia and computer vision community due to security concerns.…
GAP++: Learning to generate target-conditioned adversarial examples
Xiaofeng Mao, Yuefeng Chen, Yuhong Li +2
Adversarial examples are perturbed inputs which can cause a serious threat for machine learning models. Finding these perturbations is such a hard task that we can only use the ite…
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
Xiaodan Li, Yuefeng Chen, Yuan He +1
Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible…
Learning To Characterize Adversarial Subspaces
Xiaofeng Mao, Yuefeng Chen, Yuhong Li +2
Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially de…