  Nowadays, Deep Learning as a service can be deployed in Internet of Things
(IoT) to provide smart services and sensor data processing. However, recent
research has revealed that some Deep Neural Networks (DNN) can be easily misled
by adding relatively small but adversarial perturbations to the input (e.g.,
pixel mutation in input images). One challenge in defending DNN against these
attacks is to efficiently identifying and filtering out the adversarial pixels.
The state-of-the-art defense strategies with good robustness often require
additional model training for specific attacks. To reduce the computational
cost without loss of generality, we present a defense strategy called a
progressive defense against adversarial attacks (PDAAA) for efficiently and
effectively filtering out the adversarial pixel mutations, which could mislead
the neural network towards erroneous outputs, without a-priori knowledge about
the attack type. We evaluated our progressive defense strategy against various
attack methods on two well-known datasets. The result shows it outperforms the
state-of-the-art while reducing the cost of model training by 50% on average.
