  Image compression-based approaches for defending against the
adversarial-example attacks, which threaten the safety use of deep neural
networks (DNN), have been investigated recently. However, prior works mainly
rely on directly tuning parameters like compression rate, to blindly reduce
image features, thereby lacking guarantee on both defense efficiency (i.e.
accuracy of polluted images) and classification accuracy of benign images,
after applying defense methods. To overcome these limitations, we propose a
JPEG-based defensive compression framework, namely "feature distillation", to
effectively rectify adversarial examples without impacting classification
accuracy on benign data. Our framework significantly escalates the defense
efficiency with marginal accuracy reduction using a two-step method: First, we
maximize malicious features filtering of adversarial input perturbations by
developing defensive quantization in frequency domain of JPEG compression or
decompression, guided by a semi-analytical method; Second, we suppress the
distortions of benign features to restore classification accuracy through a
DNN-oriented quantization refine process. Our experimental results show that
proposed "feature distillation" can significantly surpass the latest
input-transformation based mitigations such as Quilting and TV Minimization in
three aspects, including defense efficiency (improve classification accuracy
from $\sim20\%$ to $\sim90\%$ on adversarial examples), accuracy of benign
images after defense ($\le1\%$ accuracy degradation), and processing time per
image ($\sim259\times$ Speedup). Moreover, our solution can also provide the
best defense efficiency ($\sim60\%$ accuracy) against the recent adaptive
attack with least accuracy reduction ($\sim1\%$) on benign images when compared
with other input-transformation based defense methods.
