Using deceptive data to trick a neural network into making wrong predictions is called adversarial attacks. Adding noise to samples is a common technique to create adversarial samples. The success of adversarial attacks is especially exaggerated as the resolution of images increases. Adding noisy samples to training data can help models recognize the weak spots in their learned decision boundary and improve their performance.
