Abstract
Although recent deep neural network algorithm has shown tremendous success in several computer vision tasks, their vulnerability against minute adversarial perturbations has raised a serious concern. In the early days of crafting these adversarial examples, artificial noises are optimized through the network and added in the images to decrease the confidence of the classifiers against the true class. However, recent efforts are showcasing the presence of natural adversarial examples which can also be effectively used to fool the deep neural networks with high confidence. In this paper, for the first time, we have raised the question that whether there is any robustness connection between artificial and natural adversarial examples. The possible robustness connection between natural and artificial adversarial examples is studied in the form that whether an adversarial example detector trained on artificial examples can detect the natural adversarial examples. We have analyzed several deep neural networks for the possible detection of artificial and natural adversarial examples in seen and unseen settings to set up a robust connection. The extensive experimental results reveal several interesting insights to defend the deep classifiers whether vulnerable against natural or artificially perturbed examples. We believe these findings can pave a way for the development of unified resiliency because defense against one attack is not sufficient for real-world use cases.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.