8 citations · 24 across the 5 of their papers we have counts for
7 papers
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…
A Large-scale Multiple-objective Method for Black-box Attack against Object Detection
Siyuan Liang, Longkang Li, Yanbo Fan +4
Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model info…
LAS-AT: Adversarial Training with Learnable Attack Strategy
Xiaojun Jia, Yong Zhang, Baoyuan Wu +3
Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial examples…
An Effective and Robust Detector for Logo Detection
Xiaojun Jia, Huanqian Yan, Yonglin Wu +3
In recent years, intellectual property (IP), which represents literary, inventions, artistic works, etc, gradually attract more and more people's attention. Particularly, with the…
Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples
Xiaojun Jia, Xingxing Wei, Xiaochun Cao +1
Recent research has demonstrated that adding some imperceptible perturbations to original images can fool deep learning models. However, the current adversarial perturbations are u…
Identifying and Resisting Adversarial Videos Using Temporal Consistency
Xiaojun Jia, Xingxing Wei, Xiaochun Cao
Video classification is a challenging task in computer vision. Although Deep Neural Networks (DNNs) have achieved excellent performance in video classification, recent research sho…