  Deep learning has demonstrated state-of-the-art performance for a variety of
challenging computer vision tasks. On one hand, this has enabled deep visual
models to pave the way for a plethora of critical applications like disease
prognostics and smart surveillance. On the other, deep learning has also been
found vulnerable to adversarial attacks, which calls for new techniques to
defend deep models against these attacks. Among the attack algorithms, the
black-box schemes are of serious practical concern since they only need
publicly available knowledge of the targeted model. We carefully analyze the
inherent weakness of deep models in black-box settings where the attacker may
develop the attack using a model similar to the targeted model. Based on our
analysis, we introduce a novel gradient regularization scheme that encourages
the internal representation of a deep model to be orthogonal to another, even
if the architectures of the two models are similar. Our unique constraint
allows a model to concomitantly endeavour for higher accuracy while maintaining
near orthogonal alignment of gradients with respect to a reference model.
Detailed empirical study verifies that controlled misalignment of gradients
under our orthogonality objective significantly boosts a model's robustness
against transferable black-box adversarial attacks. In comparison to regular
models, the orthogonal models are significantly more robust to a range of $l_p$
norm bounded perturbations. We verify the effectiveness of our technique on a
variety of large-scale models.
