9 citations · 34 across the 9 of their papers we have counts for
15 papers
GAMA: Generative Adversarial Multi-Object Scene Attacks
Abhishek Aich, Calvin-Khang Ta, Akash Gupta +4
The majority of methods for crafting adversarial attacks have focused on scenes with a single dominant object (e.g., images from ImageNet). On the other hand, natural scenes includ…
Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks
Abhishek Aich, Shasha Li, Chengyu Song +3
State-of-the-art generative model-based attacks against image classifiers overwhelmingly focus on single-object (i.e., single dominant object) images. Different from such settings,…
Zero-Query Transfer Attacks on Context-Aware Object Detectors
Zikui Cai, Shantanu Rane, Alejandro E. Brito +4
Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural…
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Shasha Li, Abhishek Aich, Shitong Zhu +4
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied. This could be possible because,…
ADC: Adversarial attacks against object Detection that evade Context consistency checks
Mingjun Yin, Shasha Li, Chengyu Song +3
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, which are slightly perturbed input images which lead DNNs to make wrong predictions. To protec…
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
Mingjun Yin, Shasha Li, Zikui Cai +4
Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in th…