  Although deep learning has shown great success in recent years, researchers
have discovered a critical flaw where small, imperceptible changes in the input
to the system can drastically change the output classification. These attacks
are exploitable in nearly all of the existing deep learning classification
frameworks. However, the susceptibility of deep sparse coding models to
adversarial examples has not been examined. Here, we show that classifiers
based on a deep sparse coding model whose classification accuracy is
competitive with a variety of deep neural network models are robust to
adversarial examples that effectively fool those same deep learning models. We
demonstrate both quantitatively and qualitatively that the robustness of deep
sparse coding models to adversarial examples arises from two key properties.
First, because deep sparse coding models learn general features corresponding
to generators of the dataset as a whole, rather than highly discriminative
features for distinguishing specific classes, the resulting classifiers are
less dependent on idiosyncratic features that might be more easily exploited.
Second, because deep sparse coding models utilize fixed point attractor
dynamics with top-down feedback, it is more difficult to find small changes to
the input that drive the resulting representations out of the correct attractor
basin.
