  Despite the enormous performance of deepneural networks (DNNs), recent
studies have shown theirvulnerability to adversarial examples (AEs), i.e.,
care-fully perturbed inputs designed to fool the targetedDNN. Currently, the
literature is rich with many ef-fective attacks to craft such AEs. Meanwhile,
many de-fenses strategies have been developed to mitigate thisvulnerability.
However, these latter showed their effec-tiveness against specific attacks and
does not general-ize well to different attacks. In this paper, we proposea
framework for defending DNN classifier against ad-versarial samples. The
proposed method is based on atwo-stage framework involving a separate detector
anda denoising block. The detector aims to detect AEs bycharacterizing them
through the use of natural scenestatistic (NSS), where we demonstrate that
these statis-tical features are altered by the presence of
adversarialperturbations. The denoiser is based on block matching3D (BM3D)
filter fed by an optimum threshold valueestimated by a convolutional neural
network (CNN) toproject back the samples detected as AEs into theirdata
manifold. We conducted a complete evaluation onthree standard datasets namely
MNIST, CIFAR-10 andTiny-ImageNet. The experimental results show that
theproposed defense method outperforms the state-of-the-art defense techniques
by improving the robustnessagainst a set of attacks under black-box, gray-box
and white-box settings. The source code is available at:
https://github.com/kherchouche-anouar/2DAE
