  Many machine learning classifiers are vulnerable to adversarial
perturbations. An adversarial perturbation modifies an input to change a
classifier's prediction without causing the input to seem substantially
different to human perception. We deploy three methods to detect adversarial
images. Adversaries trying to bypass our detectors must make the adversarial
image less pathological or they will fail trying. Our best detection method
reveals that adversarial images place abnormal emphasis on the lower-ranked
principal components from PCA. Other detectors and a colorful saliency map are
in an appendix.
