Add to ORKGDespite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible but malicious perturbations to natural inputs. These altered inputs are known in the literature as adversarial examples. In this paper we propose a novel probabilistic framework to generalize and extend adversarial attacks in order to produce a desired probability distribution for the classes when we apply the attack method to a large number of inputs. This novel attack strategy provides the attacker with greater control over the target model, and increases the complexity of detecting that the model is being atta...
Despite exhibiting unprecedented success in many application domains, machine‐learning models have b...
Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a uni...
Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease a victim'...
Deep learning plays an important role in various disciplines, such as auto-driving, information tech...
Current machine learning models achieve super-human performance in many real-world applications. Sti...
Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the...
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Neverth...
Deep learning has witnessed astonishing advancement in the last decade and revolutionized many field...
From simple time series forecasting to computer security and autonomous systems, machine learning (M...
The vulnerabilities of deep neural networks against adversarial examples have become a significant c...
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbe...
Adversarial robustness continues to be a major challenge for deep learning. A core issue is that rob...
Pre-trained language models (PLMs) that achieve success in applications are susceptible to adversari...
As deep learning become more popular and have grown to become crucial components in the daily device...
Reliable deployment of machine learning models such as neural networks continues to be challenging d...
Despite exhibiting unprecedented success in many application domains, machine‐learning models have b...
Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a uni...
Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease a victim'...
Deep learning plays an important role in various disciplines, such as auto-driving, information tech...
Current machine learning models achieve super-human performance in many real-world applications. Sti...
Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the...
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Neverth...
Deep learning has witnessed astonishing advancement in the last decade and revolutionized many field...
From simple time series forecasting to computer security and autonomous systems, machine learning (M...
The vulnerabilities of deep neural networks against adversarial examples have become a significant c...
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbe...
Adversarial robustness continues to be a major challenge for deep learning. A core issue is that rob...
Pre-trained language models (PLMs) that achieve success in applications are susceptible to adversari...
As deep learning become more popular and have grown to become crucial components in the daily device...
Reliable deployment of machine learning models such as neural networks continues to be challenging d...
Despite exhibiting unprecedented success in many application domains, machine‐learning models have b...
Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a uni...
Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease a victim'...