  The incredible effectiveness of adversarial attacks on fooling deep neural
networks poses a tremendous hurdle in the widespread adoption of deep learning
in safety and security-critical domains. While adversarial defense mechanisms
have been proposed since the discovery of the adversarial vulnerability issue
of deep neural networks, there is a long path to fully understand and address
this issue. In this study, we hypothesize that part of the reason for the
incredible effectiveness of adversarial attacks is their ability to implicitly
tap into and exploit the gradient flow of a deep neural network. This innate
ability to exploit gradient flow makes defending against such attacks quite
challenging. Motivated by this hypothesis we argue that if a deep neural
network architecture can explicitly tap into its own gradient flow during the
training, it can boost its defense capability significantly. Inspired by this
fact, we introduce the concept of self-gradient networks, a novel deep neural
network architecture designed to be more robust against adversarial
perturbations. Gradient flow information is leveraged within self-gradient
networks to achieve greater perturbation stability beyond what can be achieved
in the standard training process. We conduct a theoretical analysis to gain
better insights into the behaviour of the proposed self-gradient networks to
illustrate the efficacy of leverage this additional gradient flow information.
The proposed self-gradient network architecture enables much more efficient and
effective adversarial training, leading to faster convergence towards an
adversarially robust solution by at least 10X. Experimental results demonstrate
the effectiveness of self-gradient networks when compared with state-of-the-art
adversarial learning strategies, with 10% improvement on the CIFAR10 dataset
under PGD and CW adversarial perturbations.
