Understanding the Adversarial attacks

We'll divide this story into four sections: These are those data samples that look like normal samples but are perturbed in a certain way to fool the machine learning systems. For instance, in a given image not all the pixels are of the same importance, if you could identify the most important pixels (for the ML system to make a classification decision) and change them, your algorithm decision will also change and your sample will still appear to be normal. Why does the adversarial attack happen? Before we point out the reason as to why they happen, we just want to let you know that no ML algorithm is safe from adversarial attacks be it Logistic regression, softmax regression, SVM, Decision tree, Nearest neighbors, or deep learning models. Adversarial examples happen because of the excessive linearity in the systems.