  Advances in the development of adversarial attacks have been fundamental to
the progress of adversarial defense research. Efficient and effective attacks
are crucial for reliable evaluation of defenses, and also for developing robust
models. Adversarial attacks are often generated by maximizing standard losses
such as the cross-entropy loss or maximum-margin loss within a constraint set
using Projected Gradient Descent (PGD). In this work, we introduce a relaxation
term to the standard loss, that finds more suitable gradient-directions,
increases attack efficacy and leads to more efficient adversarial training. We
propose Guided Adversarial Margin Attack (GAMA), which utilizes function
mapping of the clean image to guide the generation of adversaries, thereby
resulting in stronger attacks. We evaluate our attack against multiple defenses
and show improved performance when compared to existing attacks. Further, we
propose Guided Adversarial Training (GAT), which achieves state-of-the-art
performance amongst single-step defenses by utilizing the proposed relaxation
term for both attack generation and training.
