Sparse Adversarial Attack via Perturbation Factorization
By Yanbo Fan, Baoyuan Wu, Tuanhui Li, Yong Zhang, Mingyang Li, Zhifeng Li, Yujiu Yang. 2020
98 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ming-Hsuan Yang
- Ajmal Saeed Mian
- Bolei Zhou
- Baoyuan Wu
- Mubarak Shah
- Navid Kardan
- Naveed Akhtar
- Yujiu Yang
In Computer Vision and Pattern Recognition
- Ming-Hsuan Yang
- Luc Van Gool
- Dacheng Tao
- Yujiu Yang
- Xiaogang Wang
- Mubarak Shah
- Andrew Zisserman
- Chen Change Loy
In Safety, Robustness & Interpretability
- Baoyuan Wu
- Mubarak Shah
- Navid Kardan
- Ajmal Saeed Mian
- Naveed Akhtar
- Zhaoxiang Zhang
- Bolei Zhou
- Ming-Hsuan Yang
Citations in the corpus
- GAN Inversion: A Survey (2021, methodology): “Adversarial attack methods [148], [149], [150], [151] aim at fooling a CNN classifier by adding a certain perturbation ∆x to a target image x.”
- Advances in adversarial attacks and defenses in computer vision: A survey (2021, methodology): “The techniques in [198] and [126] aim at perceptibility reduction of adversarial perturbations by directly focusing on `0-norm reduction of perturbation vector.”
- Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip (2020, background): “A variety of white-box attacks [9, 19, 21, 37, 40] have been developed since the vulnerability of deep neural networks discovered by Szegedy et al. [44].”