  Recent developments in the filed of Deep Learning have demonstrated that Deep
Neural Networks(DNNs) are vulnerable to adversarial examples. Specifically, in
image classification, an adversarial example can fool the well trained deep
neural networks by adding barely imperceptible perturbations to clean images.
Adversarial Training, one of the most direct and effective methods, minimizes
the losses of perturbed-data to learn robust deep networks against adversarial
attacks. It has been proven that using the fast gradient sign method (FGSM) can
achieve Fast Adversarial Training. However, FGSM-based adversarial training may
finally obtain a failed model because of overfitting to FGSM samples. In this
paper, we proposed the Diversified Initialized Perturbations Adversarial
Training (DIP-FAT) which involves seeking the initialization of the
perturbation via enlarging the output distances of the target model in a random
directions. Due to the diversity of random directions, the embedded fast
adversarial training using FGSM increases the information from the adversary
and reduces the possibility of overfitting. In addition to preventing
overfitting, the extensive results show that our proposed DIP-FAT technique can
also improve the accuracy of the clean data. The biggest advantage of DIP-FAT
method: achieving the best banlance among clean-data, perturbed-data and
efficiency.

Machine Learning;Machine Learning