On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing

Abstract

The use of pretrained models from general computer vision tasks is widespread in remote sensing, significantly reducing training costs and improving performance. However, this practice also introduces vulnerabilities to downstream tasks, where publicly available pretrained models can be used as a proxy to compromise downstream models. This paper presents a novel Adversarial Neuron Manipulation method, which generates transferable perturbations by selectively manipulating single or multiple neurons in pretrained models. Unlike existing attacks, this method eliminates the need for domain-specific information, making it more broadly applicable and efficient. By targeting multiple fragile neurons, the perturbations achieve superior attack performance, revealing critical vulnerabilities in deep learning models. Experiments on diverse models and remote sensing datasets validate the effectiveness of the proposed method. This low-access adversarial neuron manipulation technique highlights a significant security risk in transfer learning models, emphasizing the urgent need for more robust defenses in their design when addressing the safety-critical remote sensing tasks.

This work has been submitted to the IEEE for possible publication

References in corpus (8)

• AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
• Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
• SpectralGPT: Spectral Remote Sensing Foundation Model
• Learning Aerial Image Segmentation from Online Maps
• Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification
• Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
• AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
• A Billion-scale Foundation Model for Remote Sensing Images