Mitigating adversarial attacks for deep neural networks by input deformation and augmentation
Typical Deep Neural Networks (DNN) are susceptible to adversarial attacks that add
malicious perturbations to input to mislead the DNN model. Most of the state-of-theart …
malicious perturbations to input to mislead the DNN model. Most of the state-of-theart …
Mitigating Adversarial Attacks for Deep Neural Networks by Input Deformation and Augmentation
P Qiu, Q Wang, D Wang, Y Lyu, Z Lu, G Qu - … of the 25th Asia and South …, 2020 - dl.acm.org
Typical Deep Neural Networks (DNN) are susceptible to adversarial attacks that add
malicious perturbations to input to mislead the DNN model. Most of the state-of-the-art …
malicious perturbations to input to mislead the DNN model. Most of the state-of-the-art …
[PDF][PDF] Mitigating Adversarial Attacks for Deep Neural Networks by Input Deformation and Augmentation
P Qiu, Q Wang, D Wang, Y Lyu, Z Lu, G Qu - csuncle.com
Typical Deep Neural Networks (DNN) are susceptible to adversarial attacks that add
malicious perturbations to input to mislead the DNN model. Most of the state-of-theart …
malicious perturbations to input to mislead the DNN model. Most of the state-of-theart …
[PDF][PDF] Mitigating Adversarial Attacks for Deep Neural Networks by Input Deformation and Augmentation
P Qiu, Q Wang, D Wang, Y Lyu, Z Lu, G Qu - csuncle.com
Typical Deep Neural Networks (DNN) are susceptible to adversarial attacks that add
malicious perturbations to input to mislead the DNN model. Most of the state-of-theart …
malicious perturbations to input to mislead the DNN model. Most of the state-of-theart …