  DNN is presenting human-level performance for many complex intelligent tasks
in real-world applications. However, it also introduces ever-increasing
security concerns. For example, the emerging adversarial attacks indicate that
even very small and often imperceptible adversarial input perturbations can
easily mislead the cognitive function of deep learning systems (DLS). Existing
DNN adversarial studies are narrowly performed on the ideal software-level DNN
models with a focus on single uncertainty factor, i.e. input perturbations,
however, the impact of DNN model reshaping on adversarial attacks, which is
introduced by various hardware-favorable techniques such as hash-based weight
compression during modern DNN hardware implementation, has never been
discussed. In this work, we for the first time investigate the multi-factor
adversarial attack problem in practical model optimized deep learning systems
by jointly considering the DNN model-reshaping (e.g. HashNet based deep
compression) and the input perturbations. We first augment adversarial example
generating method dedicated to the compressed DNN models by incorporating the
software-based approaches and mathematical modeled DNN reshaping. We then
conduct a comprehensive robustness and vulnerability analysis of deep
compressed DNN models under derived adversarial attacks. A defense technique
named "gradient inhibition" is further developed to ease the generating of
adversarial examples thus to effectively mitigate adversarial attacks towards
both software and hardware-oriented DNNs. Simulation results show that
"gradient inhibition" can decrease the average success rate of adversarial
attacks from 87.99% to 4.77% (from 86.74% to 4.64%) on MNIST (CIFAR-10)
benchmark with marginal accuracy degradation across various DNNs.
