3 citations · 8 across the 5 of their papers we have counts for
6 papers
Progressive residual learning for single image dehazing
Yudong Liang, Bin Wang, Jiaying Liu +3
The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when…
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…
Progressive Depth Learning for Single Image Dehazing
Yudong Liang, Bin Wang, Jiaying Liu +3
The formulation of the hazy image is mainly dominated by the reflected lights and ambient airlight. Existing dehazing methods often ignore the depth cues and fail in distant areas…
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
Yaguan Qian, Qiqi Shao, Jiamin Wang +5
With the boom of edge intelligence, its vulnerability to adversarial attacks becomes an urgent problem. The so-called adversarial example can fool a deep learning model on the edge…
TEAM: We Need More Powerful Adversarial Examples for DNNs
Yaguan Qian, Ximin Zhang, Bin Wang +4
Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassificati…
Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks
Ya-guan Qian, Dan-feng Ma, Bin Wang +5
Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work…