  Neural networks are being applied in many tasks related to IoT with
encouraging results. For example, neural networks can precisely detect human,
objects and animal via surveillance camera for security purpose. However,
neural networks have been recently found vulnerable to well-designed input
samples that called adversarial examples. Such issue causes neural networks to
misclassify adversarial examples that are imperceptible to humans. We found
giving a rotation to an adversarial example image can defeat the effect of
adversarial examples. Using MNIST number images as the original images, we
first generated adversarial examples to neural network recognizer, which was
completely fooled by the forged examples. Then we rotated the adversarial image
and gave them to the recognizer to find the recognizer to regain the correct
recognition. Thus, we empirically confirmed rotation to images can protect
pattern recognizer based on neural networks from adversarial example attacks.
