Adversarial sampling for fairness testing in deep neural network
In this research, we focus on the usage of adversarial sampling to test for the fairness in the
prediction of deep neural network model across different classes of image in a given …
prediction of deep neural network model across different classes of image in a given …
Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features
Skin cancer is the most common type of cancer in United States with 9,500 new cases
diagnosed daily. It is one of the deadliest forms, however early detection and treatments can …
diagnosed daily. It is one of the deadliest forms, however early detection and treatments can …
[PDF][PDF] Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network
O Smith, A Brown - … Journal of Research and Innovation in Applied …, 2023 - researchgate.net
In adversarial machine learning, attackers add carefully crafted perturbations to input, where
the perturbations are almost imperceptible to humans, but can cause models to make wrong …
the perturbations are almost imperceptible to humans, but can cause models to make wrong …
Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network
S Oliver, B Anderson - 2023 - philpapers.org
In adversarial machine learning, attackers add carefully crafted perturbations to input, where
the perturbations are almost imperceptible to humans, but can cause models to make wrong …
the perturbations are almost imperceptible to humans, but can cause models to make wrong …
Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network
A Brown - 2023 - philpapers.org
In adversarial machine learning, attackers add carefully crafted perturbations to input, where
the perturbations are almost imperceptible to humans, but can cause models to make wrong …
the perturbations are almost imperceptible to humans, but can cause models to make wrong …
[引用][C] INVESTİGATİON ON DETECTİON OF DATA POİSONİNG ATTACKS: MOST POSSİBLE DEFENCES AND COUNTER MEASURES
K Muppavaram, A Shivampeta, H Biruduraju…