  While neural networks allow highly accurate predictions in many tasks, their
lack of robustness towards even slight input perturbations often hampers their
deployment. Adversarial attacks such as the seminal projected gradient descent
(PGD) offer an effective means to evaluate a model's robustness and dedicated
solutions have been proposed for attacks on semantic segmentation or optical
flow estimation. While they attempt to increase the attack's efficiency, a
further objective is to balance its effect, so that it acts on the entire image
domain instead of isolated point-wise predictions. This often comes at the cost
of optimization stability and thus efficiency. Here, we propose CosPGD, an
attack that encourages more balanced errors over the entire image domain while
increasing the attack's overall efficiency. To this end, CosPGD leverages a
simple alignment score computed from any pixel-wise prediction and its target
to scale the loss in a smooth and fully differentiable way. It leads to
efficient evaluations of a model's robustness for semantic segmentation as well
as regression models (such as optical flow, disparity estimation, or image
restoration), and it allows it to outperform the previous SotA attack on
semantic segmentation. We provide code for the CosPGD algorithm and example
usage at https://github.com/shashankskagnihotri/cospgd.
