# Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNet

Source: [Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNet](<https://www.sota2.com/research/paper/68fa70cd1e209131cea4d409>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:26:19.199Z

Authors: Yunuo Xiong, Shujuan Liu, Hongwei Xiong

Published: 2023-09-03T14:20:58.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.05947>)

[PDF](<https://arxiv.org/pdf/2310.05947.pdf>)