A survey of adversarial attacks on machine learning

Recently, machine learning has a potential to upgrade existing technology areas to a new level. In many areas like vision, security, natural language processing, and speech recognition, machine learning algorithms have been implemented to both enhance the service performance and create new services. However, these systems contain critical vulnerabilities and they are subject to adversarial attacks. In this paper, we propose a novel survey that highlights the effects of adversarial attacks during the training stage. We investigate the attacks from both the attacker and target perspectives. We divide attacks into two main categories according to their design strategies, namely poison and backdoor attacks. In each attack category, we use the targeted environment to analyze attacks with a systematic approach. Finally, we conclude the paper with challenges and future work that may help researchers to understand the anatomy of adversarial attacks. The ultimate goal of this survey is to show research directions for creating effective countermeasures against adversarial attacks on machine learning. Thus, societies will benefit from secure services that use machine learning models.