Detecting and mitigating adversarial perturbations for robust face recognition
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
[PDF][PDF] Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, N Ratha, R Singh, M Vatsa - researchgate.net
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, N Ratha, R Singh… - International Journal of …, 2019 - infona.pl
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition.
G Goswami, A Agarwal, N Ratha… - … Journal of Computer …, 2019 - search.ebscohost.com
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, N Ratha… - … Journal of Computer …, 2019 - search.proquest.com
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, N Ratha, R Singh, M Vatsa - 2019 - dl.acm.org
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
[PDF][PDF] Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, N Ratha, R Singh, M Vatsa - iab-rubric.org
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, NK Ratha, R Singh, M Vatsa - IJCV, 2019 - research.ibm.com
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …
[PDF][PDF] Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
G Goswami, A Agarwal, N Ratha, R Singh, M Vatsa - iab-rubric.org
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …
learning capacity. However, they are essentially a black box method since it is not easy to …