Robust and Efficient Interference Neural Networks for Defending Against
  Adversarial Attacks in ImageNet
Yunuo Xiong Shujuan Liu Hongwei Xiong
The existence of adversarial images has seriously affected the task of image
recognition and practical application of deep learning, it is also a key
scientific problem that deep learning urgently needs to solve. By far the most
effective approach is to train the neural network with a large number of
adversarial examples. However, this adversarial training method requires a huge
amount of computing resources when applied to ImageNet, and has not yet
achieved satisfactory results for high-intensity adversarial attacks. In this
paper, we construct an interference neural network by applying additional
background images and corresponding labels, and use pre-trained ResNet-152 to
efficiently complete the training. Compared with the state-of-the-art results
under the PGD attack, it has a better defense effect with much smaller
computing resources. This work provides new ideas for academic research and
practical applications of effective defense against adversarial attacks.