AI Security for Geoscience and Remote Sensing: Challenges and future trends

Xu, Yonghao; Bai, Tao; Yu, Weikang; Chang, Shizhen; Atkinson, Peter M.; Ghamisi, Pedram

doi:10.1109/mgrs.2023.3272825

Cited by 26 publications

(2 citation statements)

References 126 publications

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“…Ref. [35] introduced the artificial intelligence challenges and prospects in the field of remote sensing.…”

Section: Adversarial Attacks In Remote Sensingmentioning

confidence: 99%

Attention-Enhanced One-Shot Attack against Single Object Tracking for Unmanned Aerial Vehicle Remote Sensing Images

Jiang,

Yin

2023

Remote Sensing

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Recent studies have shown that deep-learning-based models for processing Unmanned Aerial Vehicle (UAV) remote sensing images are vulnerable to artificially designed adversarial examples, which can lead to incorrect predictions of deep models when facing adversarial examples. Previous adversarial attack methods have mainly focused on the classification and detection of UAV remote sensing images, and there is still a lack of research on adversarial attacks for object tracking in UAV video. To address this challenge, we propose an attention-enhanced one-shot adversarial attack method for UAV remote sensing object tracking, which perturbs only the template frame and generates adversarial samples offline. First, we employ an attention feature loss to make the original frame’s features dissimilar to those of the adversarial frame, and an attention confidence loss to either suppress or enhance different confidence scores. Additionally, by forcing the tracker to concentrate on the background information near the target, a background distraction loss is used to mismatch templates with subsequent frames. Finally, we add total variation loss to generate adversarial examples that appear natural to humans. We validate the effectiveness of our method against popular trackers such as SiamRPN, DaSiamRPN, and SiamRPN++ on the UAV123 remote sensing dataset. Experimental results verify the superior attack performance of our proposed method.

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“…Ref. [35] introduced the artificial intelligence challenges and prospects in the field of remote sensing.…”

Section: Adversarial Attacks In Remote Sensingmentioning

confidence: 99%

Attention-Enhanced One-Shot Attack against Single Object Tracking for Unmanned Aerial Vehicle Remote Sensing Images

Jiang,

Yin

2023

Remote Sensing

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“…How to effectively and automatically extract features with learning capabilities is a hot topic in current research. With the development of artificial intelligence, the successful application of artificial intelligence covers almost all aspects of Earth-observation missions [16]. Artificial intelligence is also of great use in SAR data [17].…”

Section: Introductionmentioning

confidence: 99%

JPSSL: SAR Terrain Classification Based on Jigsaw Puzzles and FC-CRF

Ren,

Lu,

Hou

et al. 2024

Remote Sensing

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Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

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