# Demotivate adversarial defense in remote sensing Source: [Demotivate adversarial defense in remote sensing]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:13:37.567Z Authors: Adrien Chan-Hon-Tong, Gaston Lenczner, Aurelien Plyer Published: 2021-05-28T15:04:37.000Z ## Abstract Convolutional neural networks are currently the state-of-the-art algorithms for many remote sensing applications such as semantic segmentation or object detection. However, these algorithms are extremely sensitive to over-fitting, domain change and adversarial examples specifically designed to fool them. While adversarial attacks are not a threat in most remote sensing applications, one could wonder if strengthening networks to adversarial attacks could also increase their resilience to over-fitting and their ability to deal with the inherent variety of worldwide data. In this work, we study both adversarial retraining and adversarial regularization as adversarial defenses to this purpose. However, we show through several experiments on public remote sensing datasets that adversarial robustness seems uncorrelated to geographic and over-fitting robustness. ## 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]() [PDF]()