Deep neural networks (DNNs) have achieved great success in image
classification, but they may be very vulnerable to adversarial attacks with
small perturbations to images. Moreover, the adversarial training based on
adversarial image samples has been shown to improve the robustness and
generalization of DNNs. The aim of this paper is to develop a novel framework
based on information-geometry sensitivity analysis and the particle swarm
optimization to improve two aspects of adversarial image generation and
training for DNNs. The first one is customized generation of adversarial
examples. It can design adversarial attacks from options of the number of
perturbed pixels, the misclassification probability, and the targeted incorrect
class, and hence it is more flexible and effective to locate vulnerable pixels
and also enjoys certain adversarial universality. The other is targeted
adversarial training. DNN models can be improved in training with the
adversarial information using a manifold-based influence measure effective in
vulnerable image/pixel detection as well as allowing for targeted attacks,
thereby exhibiting an enhanced adversarial defense in testing