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The field of adversarial machine learning has experienced a near exponentialgrowth in the amount of papers being produced since 2018. This massiveinformation output has yet to be properly processed and categorized. In thispaper, we seek to help alleviate this problem by systematizing the recentadvances in adversarial machine learning black-box attacks since 2019. Oursurvey summarizes and categorizes 20 recent black-box attacks. We also presenta new analysis for understanding the attack success rate with respect to theadversarial model used in each paper. Overall, our paper surveys a wide body ofliterature to highlight recent attack developments and organizes them into fourattack categories: score based attacks, decision based attacks, transferattacks and non-traditional attacks. Further, we provide a new mathematicalframework to show exactly how attack results can fairly be compared.

Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks