Detection of adversarial examples in deep neural networks with natural scene statistics
A Kherchouche, SA Fezza… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Recent studies have demonstrated that the deep neural networks (DNNs) are vulnerable to
carefully-crafted perturbations added to a legitimate input image. Such perturbed images are …
carefully-crafted perturbations added to a legitimate input image. Such perturbed images are …
Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation
In recent years, classification techniques based on deep neural networks (DNN) were widely
used in many fields such as computer vision, natural language processing, and self-driving …
used in many fields such as computer vision, natural language processing, and self-driving …
Natural scene statistics for detecting adversarial examples in deep neural networks
A Kherchouche, SA Fezza… - 2020 IEEE 22nd …, 2020 - ieeexplore.ieee.org
The deep neural networks (DNNs) have been adopted in a wide spectrum of applications.
However, it has been demonstrated that their are vulnerable to adversarial examples (AEs) …
However, it has been demonstrated that their are vulnerable to adversarial examples (AEs) …
A Detailed Study on Adversarial Attacks and Defense Mechanisms on Various Deep Learning Models
KV Priya, PJ Dinesh - 2023 Advanced Computing and …, 2023 - ieeexplore.ieee.org
With the increased computational efficiency, Deep Neural Network gained more importance
in the area of medical diagnosis. Nowadays many researchers have noticed the security …
in the area of medical diagnosis. Nowadays many researchers have noticed the security …
Detection of Iterative Adversarial Attacks via Counter Attack
Deep neural networks (DNNs) have proven to be powerful tools for processing unstructured
data. However, for high-dimensional data, like images, they are inherently vulnerable to …
data. However, for high-dimensional data, like images, they are inherently vulnerable to …