  Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated
by imposing subtle perturbations to inputs that lead a model to predict
incorrect outputs. Currently, a large number of researches on defending
adversarial examples pay little attention to the real-world applications,
either with high computational complexity or poor defensive effects. Motivated
by this observation, we develop an efficient preprocessing method to defend
adversarial images. Specifically, before an adversarial example is fed into the
model, we perform two image transformations: WebP compression, which is
utilized to remove the small adversarial noises. Flip operation, which flips
the image once along one side of the image to destroy the specific structure of
adversarial perturbations. Finally, a de-perturbed sample is obtained and can
be correctly classified by DNNs. Experimental results on ImageNet show that our
method outperforms the state-of-the-art defense methods. It can effectively
defend adversarial attacks while ensure only very small accuracy drop on normal
images.
