A Cycle Gan Approach for Heterogeneous Domain Adaptation in Land Use Classification

Voreiter, Claire; Burnel, Jean-Christophe; Lassalle, Pierre; Spigai, Marc; Hugues, Romain; Courty, Nicolas

doi:10.1109/igarss39084.2020.9324264

Cited by 10 publications

(1 citation statement)

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“…[30] also used GANs for learning representation by incorporating a non-local layer into their architecture. GANs have also been used in remote sensing for domain adaptation on hetereogenous data [31] which might be seen as transfer learning with different type of data.…”

Section: Related Workmentioning

confidence: 99%

Generating Natural Adversarial Remote Sensing Images

Burnel

Fatras

Flamary

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Over the last years, Remote Sensing Images (RSI) analysis have started resorting to using deep neural networks to solve most of the commonly faced problems, such as detection, land cover classification or segmentation. As far as critical decision making can be based upon the results of RSI analysis, it is important to clearly identify and understand potential security threats occurring in those machine learning algorithms. Notably, it has recently been found that neural networks are particularly sensitive to carefully designed attacks, generally crafted given the full knowledge of the considered deep network. In this paper, we consider the more realistic but challenging case where one wants to generate such attacks in the case of a black-box neural network. In this case, only the prediction score of the network is accessible, given a specific input. Examples that lure away the network's prediction, while being perceptually similar to real images, are called natural or unrestricted adversarial examples. We present an original method to generate such examples, based on a variant of the Wasserstein Generative Adversarial Network. We demonstrate its effectiveness on natural adversarial hyperspectral image generation and image modification for fooling a state-of-the-art detector. Among others, we also conduct a perceptual evaluation with human annotators to better assess the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%