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Today's state-of-the-art image classifiers fail to correctly classifycarefully manipulated adversarial images. In this work, we develop a new,localized adversarial attack that generates adversarial examples byimperceptibly altering the backgrounds of normal images. We first use thisattack to highlight the unnecessary sensitivity of neural networks to changesin the background of an image, then use it as part of a new training technique:localized adversarial training. By including locally adversarial images in thetraining set, we are able to create a classifier that suffers less loss than anon-adversarially trained counterpart model on both natural and adversarialinputs. The evaluation of our localized adversarial training algorithm on MNISTand CIFAR-10 datasets shows decreased accuracy loss on natural images, andincreased robustness against adversarial inputs.

Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification