38 citations · 241 across the 28 of their papers we have counts for
3 papers · 1 filter
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack
Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Recent development in the field of Deep Learning have exposed the underlying vulnerability of Deep Neural Network (DNN) against adversarial examples. In image classification, an ad…
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions
Adnan Siraj Rakin, Jinfeng Yi, Boqing Gong +1
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks. To this end, many defense approaches that attempt to improve the robustness of DNN…
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Adnan Siraj Rakin, Zhezhi He, Boqing Gong +1
Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend again…