Authors:
Anastasios Nikolakopoulos
;
Achilleas Marinakis
;
Vrettos Moulos
and
Theodora Varvarigou
Affiliation:
School of Electrical & Computer Engineering, National Technical University of Athens, Greece
Keyword(s):
Adversarial Attacks, Adversarial Defenses, Adversarial Examples, Deep Neural Networks, Machine Learning, Data Analysis, Artificial Intelligence.
Abstract:
In the scientific community of Machine Learning and Artificial Intelligence, Adversarial Attacks are evolving to an emerging issue. Carefully perturbed data samples invade to deep neural networks and cause problems, such as misclassifications and false / malformed outputs. The community has proposed multiple defense strategies, in order to overcome this problem. This paper summarizes the existing (and most well-known) adversarial attacks & defenses. Then, it proposes a potential solution to the issue, with a theoretical approach of an antivirus software scenario, the Hoplite Antivirus. This approach could be a vital step towards addressing the constantly evolving adversarial attacks, taking a note from the way software scientists defended (and keep defending) against computer viruses.