  Studies show that neural networks are susceptible to adversarial attacks.
This exposes a potential threat to neural network-based artificial intelligence
systems. We observe that the probability of the correct result outputted by the
neural network increases by applying small perturbations generated for
non-predicted class labels to adversarial examples. Based on this observation,
we propose a method of counteracting adversarial perturbations to resist
adversarial examples. In our method, we randomly select a number of class
labels and generate small perturbations for these selected labels. The
generated perturbations are added together and then clamped onto a specified
space. The obtained perturbation is finally added to the adversarial example to
counteract the adversarial perturbation contained in the example. The proposed
method is applied at inference time and does not require retraining or
finetuning the model. We validate the proposed method on CIFAR-10 and
CIFAR-100. The experimental results demonstrate that our method effectively
improves the defense performance of the baseline methods, especially against
strong adversarial examples generated using more iterations.
