2020

Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Silva, Samuel Henrique, Najafirad, Peyman

Understand

As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable.

This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms.
We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses.
We survey the most recent and important results in adversarial example generation, defense mechanisms with adversarial (re)Training as their main defense against perturbations.

Built on

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