  Deep neural networks (DNNs) have achieved excellent performance on several
tasks and have been widely applied in both academia and industry. However, DNNs
are vulnerable to adversarial machine learning attacks, in which noise is added
to the input to change the network output. We have devised an
image-processing-based method to detect adversarial images based on our
observation that adversarial noise is reduced after applying these operations
while the normal images almost remain unaffected. In addition to detection,
this method can be used to restore the adversarial images' original labels,
which is crucial to restoring the normal functionalities of DNN-based systems.
Testing using an adversarial machine learning database we created for
generating several types of attack using images from the ImageNet Large Scale
Visual Recognition Challenge database demonstrated the efficiency of our
proposed method for both detection and correction.
