Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark (2202.07054v3)

Abstract: Deep neural networks have achieved great success in many important remote sensing tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In this study, we systematically analyze the universal adversarial examples in remote sensing data for the first time, without any knowledge from the victim model. Specifically, we propose a novel black-box adversarial attack method, namely Mixup-Attack, and its simple variant Mixcut-Attack, for remote sensing data. The key idea of the proposed methods is to find common vulnerabilities among different networks by attacking the features in the shallow layer of a given surrogate model. Despite their simplicity, the proposed methods can generate transferable adversarial examples that deceive most of the state-of-the-art deep neural networks in both scene classification and semantic segmentation tasks with high success rates. We further provide the generated universal adversarial examples in the dataset named UAE-RS, which is the first dataset that provides black-box adversarial samples in the remote sensing field. We hope UAE-RS may serve as a benchmark that helps researchers to design deep neural networks with strong resistance toward adversarial attacks in the remote sensing field. Codes and the UAE-RS dataset are available online (https://github.com/YonghaoXu/UAE-RS).

• The paper presents novel Mixup-Attack and Mixcut-Attack methodologies that achieve high transferability in deceiving deep neural networks.
• It establishes the UAE-RS dataset as the first benchmark for assessing black-box adversarial examples in remote sensing across multiple datasets.
• Results highlight the urgent need for robust adversarial defenses in DNN-based remote sensing applications to ensure operational security.

Overview of 'Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark'

The paper under discussion addresses a critical aspect of the application of deep neural networks (DNNs) in remote sensing: their vulnerability to adversarial examples. While DNNs significantly impact tasks like scene classification and semantic segmentation, their susceptibility to adversarial attacks poses a notable security risk. This paper systematically investigates universal adversarial examples in remote sensing data, with the primary contribution being the development of a novel black-box adversarial attack method called Mixup-Attack and its variant Mixcut-Attack. The research also introduces the UAE-RS dataset, which serves as a benchmark for evaluating black-box adversarial samples in remote sensing.

Key Contributions and Methods

1. Methodological Innovation: The paper presents Mixup-Attack and Mixcut-Attack, which target the common vulnerabilities among different DNN models. These methods generate adversarial examples by manipulating features in the shallow layers of surrogate models. This approach allows the adversarial samples to maintain high transferability across various architectures, achieving significant success rates in fooling state-of-the-art DNNs without prior knowledge of the victim models.
2. Benchmark Dataset: UAE-RS is established as the first dataset providing black-box adversarial examples specific to remote sensing. This dataset is envisioned to foster research aiming to design DNNs with enhanced resistance to adversarial attacks.
3. Numerical Results: The proposed methods exhibit substantial success rates in deceiving several leading deep learning models across widely used datasets like UCM, AID, Vaihingen, and Zurich Summer. For instance, Mixup-Attack and Mixcut-Attack outperform traditional methods, such as FGSM and I-FGSM, when attacking models unknown to the attacker.

Implications and Future Speculations

The paper underscores the necessity of robust adversarial defense mechanisms in DNNs for remote sensing, as these models are integral to many critical applications, including environmental monitoring, urban planning, and resource management. The high transferability of adversarial examples illustrated by the authors indicates potential weaknesses in current deep learning architectures.

Practically, this reinforces the need for embedding robustness against adversarial attacks in the model design phase rather than as a superficial post hoc adjustment. From a theoretical standpoint, the universal nature of adversarial perturbations highlighted in this paper suggests that fundamental revisions to model architecture and training paradigms may be necessary.

In the context of future developments, leveraging the UAE-RS dataset could inspire new research directions. One potential area is the exploration of adversarial training techniques that utilize such datasets for enhanced generalization and robustness. Moreover, the demonstrated difference in attack resistance among architectures suggests that architectures maximizing global context awareness and depth might offer a viable pathway for increasing resilience against adversarial perturbations.

Conclusion

In conclusion, this paper provides a structured methodology and comprehensive analysis of adversarial attacks within the remote sensing domain. Its findings contribute significantly to our understanding of model vulnerabilities and serve as a catalyst for developing more secure DNN-based remote sensing systems. The introduction of UAE-RS as a benchmark also sets a foundation for continuous improvement and innovation in adversarial defenses.