  Adversarial training is exploited to develop a robust Deep Neural Network
(DNN) model against the malicious altered data. These attacks may have
catastrophic effects on DNN models but are indistinguishable for a human being.
For example, an external attack can modify an image adding noises invisible for
a human eye, but a DNN model misclassified the image. A key objective for
developing robust DNN models is to use a learning algorithm that is fast but
can also give model that is robust against different types of adversarial
attacks. Especially for adversarial training, enormously long training times
are needed for obtaining high accuracy under many different types of
adversarial samples generated using different adversarial attack techniques.
  This paper aims at accelerating the adversarial training to enable fast
development of robust DNN models against adversarial attacks. The general
method for improving the training performance is the hyperparameters
fine-tuning, where the learning rate is one of the most crucial
hyperparameters. By modifying its shape (the value over time) and value during
the training, we can obtain a model robust to adversarial attacks faster than
standard training.
  First, we conduct experiments on two different datasets (CIFAR10, CIFAR100),
exploring various techniques. Then, this analysis is leveraged to develop a
novel fast training methodology, AccelAT, which automatically adjusts the
learning rate for different epochs based on the accuracy gradient. The
experiments show comparable results with the related works, and in several
experiments, the adversarial training of DNNs using our AccelAT framework is
conducted up to 2 times faster than the existing techniques. Thus, our findings
boost the speed of adversarial training in an era in which security and
performance are fundamental optimization objectives in DNN-based applications.
