Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNet
Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Xiong, Yunuo, Liu, Shujuan, Xiong, Hongwei
Institution: Hubei Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: 超冷原子系统的相变及其普适行为研究, G6098025898
OpenAlex ID: W4387560641