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Machine learning models are critically susceptible to evasion attacks fromadversarial examples. Generally, adversarial examples, modified inputsdeceptively similar to the original input, are constructed under whiteboxsettings by adversaries with full access to the model. However, recent attackshave shown a remarkable reduction in query numbers to craft adversarialexamples using blackbox attacks. Particularly, alarming is the ability toexploit the classification decision from the access interface of a trainedmodel provided by a growing number of Machine Learning as a Service providersincluding Google, Microsoft, IBM and used by a plethora of applicationsincorporating these models. The ability of an adversary to exploit only thepredicted label from a model to craft adversarial examples is distinguished asa decision-based attack. In our study, we first deep dive into recentstate-of-the-art decision-based attacks in ICLR and SP to highlight the costlynature of discovering low distortion adversarial employing gradient estimationmethods. We develop a robust query efficient attack capable of avoidingentrapment in a local minimum and misdirection from noisy gradients seen ingradient estimation methods. The attack method we propose, RamBoAttack,exploits the notion of Randomized Block Coordinate Descent to explore thehidden classifier manifold, targeting perturbations to manipulate onlylocalized input features to address the issues of gradient estimation methods.Importantly, the RamBoAttack is more robust to the different sample inputsavailable to an adversary and the targeted class. Overall, for a given targetclass, RamBoAttack is demonstrated to be more robust at achieving a lowerdistortion within a given query budget. We curate our extensive results usingthe large-scale high-resolution ImageNet dataset and open-source our attack,test samples and artifacts on GitHub.

RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit