Adversarial Attack using Neural Image Modification

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.

Adversarial Attack using Neural Image Modification

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.

Adversarial Attack using Neural Image Modification

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.