  Recent studies have shown that deep convolutional neural networks (DCNN) are
vulnerable to adversarial examples and sensitive to perceptual quality as well
as the acquisition condition of images. These findings raise a big concern for
the adoption of DCNN-based applications for critical tasks. In the literature,
various defense strategies have been introduced to increase the robustness of
DCNN, including re-training an entire model with benign noise injection,
adversarial examples, or adding extra layers. In this paper, we investigate the
connection between adversarial manipulation and image quality, subsequently
propose a protective mechanism that doesnt require re-training a DCNN. Our
method combines image quality assessment with knowledge distillation to detect
input images that would trigger a DCCN to produce egregiously wrong results.
Using the ResNet model trained on ImageNet as an example, we demonstrate that
the detector can effectively identify poor quality and adversarial images.
