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.