Search Space of Adversarial Perturbations against Image Filters
This addresses the problem of improving offensive methods to deceive defensive measures in deep learning systems, which is incremental as it builds on existing adversarial attack research.
The study investigated the ability to create adversarial patterns in search space against defensive methods using image filters, finding a correlation between the search space of adversarial perturbations and filters on the ImageNet dataset.
The superiority of deep learning performance is threatened by safety issues for itself. Recent findings have shown that deep learning systems are very weak to adversarial examples, an attack form that was altered by the attacker's intent to deceive the deep learning system. There are many proposed defensive methods to protect deep learning systems against adversarial examples. However, there is still a lack of principal strategies to deceive those defensive methods. Any time a particular countermeasure is proposed, a new powerful adversarial attack will be invented to deceive that countermeasure. In this study, we focus on investigating the ability to create adversarial patterns in search space against defensive methods that use image filters. Experimental results conducted on the ImageNet dataset with image classification tasks showed the correlation between the search space of adversarial perturbation and filters. These findings open a new direction for building stronger offensive methods towards deep learning systems.