Adversarial Attacks and Defences Competition

Recent advances in machine learning and deep neural networks enabled researchers to solve multiple important practical problems like image, video, text classification and others. However most existing machine learning classifiers are highly vulnerable to adversarial examples [2, 39, 15, 29]. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model.