  Deep neural networks have been known to be vulnerable to adversarial
examples, which are inputs that are modified slightly to fool the network into
making incorrect predictions. This has led to a significant amount of research
on evaluating the robustness of these networks against such perturbations. One
particularly important robustness metric is the robustness to minimal l2
adversarial perturbations. However, existing methods for evaluating this
robustness metric are either computationally expensive or not very accurate. In
this paper, we introduce a new family of adversarial attacks that strike a
balance between effectiveness and computational efficiency. Our proposed
attacks are generalizations of the well-known DeepFool (DF) attack, while they
remain simple to understand and implement. We demonstrate that our attacks
outperform existing methods in terms of both effectiveness and computational
efficiency. Our proposed attacks are also suitable for evaluating the
robustness of large models and can be used to perform adversarial training (AT)
to achieve state-of-the-art robustness to minimal l2 adversarial perturbations.
