In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks,
this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial
sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted
and targeted attacks with good success rates. This method also shows some effectiveness against defensive
distillation, but not transferrable between multiple models.
In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks,
this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial
sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted
and targeted attacks with good success rates. This method also shows some effectiveness against defensive
distillation, but not transferrable between multiple models.
In order to help development into analyzing the characteristics of adversarial sample generation in artificial neural networks,
this work proposes a framework for an adversarial attack that utilizes neural image modification to generate an adversarial
sample. This method proves to be effective in reducing a target network’s accuracy in both untargeted
and targeted attacks with good success rates. This method also shows some effectiveness against defensive
distillation, but not transferrable between multiple models.
A black-box adversarial attack strategy with adjustable sparsity and generalizability for deep image classifiers
