  Deep neural networks have demonstrated high accuracy in image classification
tasks. However, they were shown to be weak against adversarial examples: a
small perturbation in the image which changes the classification output
dramatically. In recent years, several defenses have been proposed to solve
this issue in supervised classification tasks. We propose a method to obtain
robust features in unsupervised learning tasks against adversarial attacks. Our
method differs from existing solutions by directly learning the robust features
without the need to project the adversarial examples in the original examples
distribution space. A first auto-encoder A1 is in charge of perturbing the
input image to fool another auto-encoder A2 which is in charge of regenerating
the original image. A1 tries to find the less perturbed image under the
constraint that the error in the output of A2 should be at least equal to a
threshold. Thanks to this training, the encoder of A2 will be robust against
adversarial attacks and could be used in different tasks like classification.
Using state-of-art network architectures, we demonstrate the robustness of the
features obtained thanks to this method in classification tasks.
