  Machine learning techniques are immensely deployed in both industry and
academy. Recent studies indicate that machine learning models used for
classification tasks are vulnerable to adversarial examples, which limits the
usage of applications in the fields with high precision requirements. We
propose a new approach called Feature Map Denoising to detect the adversarial
inputs and show the performance of detection on the mixed dataset consisting of
adversarial examples generated by different attack algorithms, which can be
used to associate with any pre-trained DNNs at a low cost. Wiener filter is
also introduced as the denoise algorithm to the defense model, which can
further improve performance. Experimental results indicate that good accuracy
of detecting the adversarial examples can be achieved through our Feature Map
Denoising algorithm.
