Add to ORKGRobust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave. Adversarial attacks threaten the robustness of neural network classifiers, causing them to consistently and confidently misidentify road signs. One such class of attack, shadow-based attacks, causes misidentifications by applying a natural-looking shadow to input images, resulting in road signs that appear natural to a human observer but confusing for these classifiers. Current defenses against such attacks use a simple adversarial training procedure to achieve a rather low 25\% and 40\% robustness on the GTSRB and LISA test sets, respectively. In this paper, we propose a robust, fast, and generalizabl...
Despite the great achievements made by neural networks on tasks such as image classification, they a...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
Deep learning has improved the performance of many computer vision tasks. However, the features that...
Physical adversarial attacks on road signs are continuously exploiting vulnerabilities in modern day...
The literature on adversarial attacks in computer vision typically focuses on pixel-level perturbati...
Recently, much attention in the literature has been given to adversarial examples\u27\u27, input da...
Adversarial attacks can make deep neural network (DNN) models predict incorrect output labels, such ...
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, espe...
Despite the high quality performance of the deep neural network in real-world applications, they are...
A growing body of work has shown that deep neural networks are susceptible to adversarial examples. ...
Deep neural networks (DNNs) have become a powerful tool for image classification tasks in recent yea...
Most researchers have tried to enhance the robustness of DNNs by revealing and repairing the vulnera...
Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversari...
The vulnerabilities of deep neural networks against adversarial examples have become a significant c...
The widespread adoption of machine learning, especially Deep Neural Networks (DNNs) in daily life, c...
Despite the great achievements made by neural networks on tasks such as image classification, they a...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
Deep learning has improved the performance of many computer vision tasks. However, the features that...
Physical adversarial attacks on road signs are continuously exploiting vulnerabilities in modern day...
The literature on adversarial attacks in computer vision typically focuses on pixel-level perturbati...
Recently, much attention in the literature has been given to adversarial examples\u27\u27, input da...
Adversarial attacks can make deep neural network (DNN) models predict incorrect output labels, such ...
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, espe...
Despite the high quality performance of the deep neural network in real-world applications, they are...
A growing body of work has shown that deep neural networks are susceptible to adversarial examples. ...
Deep neural networks (DNNs) have become a powerful tool for image classification tasks in recent yea...
Most researchers have tried to enhance the robustness of DNNs by revealing and repairing the vulnera...
Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversari...
The vulnerabilities of deep neural networks against adversarial examples have become a significant c...
The widespread adoption of machine learning, especially Deep Neural Networks (DNNs) in daily life, c...
Despite the great achievements made by neural networks on tasks such as image classification, they a...
As modern technology is rapidly progressing, more applications are utilizing aspects of machine lear...
Deep learning has improved the performance of many computer vision tasks. However, the features that...