Adversarial_Attack_using_Neural_Image_Modification - Jan 17.pdf (12.45 MB)
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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.