# Universal adversarial perturbation for remote sensing images

arXiv: https://arxiv.org/abs/2202.10693 · HTML: https://mlsanity.com/paper/2202.10693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Spatially Transformed Adversarial Examples](https://mlsanity.com/paper/1801.02612/) (2018)
- [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://mlsanity.com/paper/1706.03424/) (2017)
- [Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark](https://mlsanity.com/paper/2202.07054/) (2022)
- [Adversarial Examples in Remote Sensing](https://mlsanity.com/paper/1805.10997/) (2018)
- [Generalizing Universal Adversarial Attacks Beyond Additive Perturbations](https://mlsanity.com/paper/2010.07788/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)

## Abstract (arXiv)

Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%.