Adversarial pan-sharpening attacks for object detection in remote sensing

Wei, Xingxing; Yuan, Maoxun

doi:10.1016/j.patcog.2023.109466

Cited by 18 publications

(6 citation statements)

References 15 publications

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“…Rust-Style Patch [45] works on improving the natural and robust adversarial patches by utilizing style transfer on remote sensing, and the authors conducted experiments in both the digital and physical domains. Wei et al [46] proposed a new way to utilize pan-sharpened images to attack object detectors. While there have been studies on adversarial attacks in the context of classification and detection tasks for remote sensing images, there has been little research on adversarial attacks for UAV remote sensing video for object tracking.…”

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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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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“…Digital Object Identifier 10.1109/JSTARS.2023.3327169 obtained remote sensing images, such as panchromatic (PAN) and multispectral (MS) images [1]. PAN images include comprehensive spatial information that is used for object detection and recognition [2]. MS images feature a variety of spectral bands that are applied to image classification [1], [3].…”

Section: Indexmentioning

confidence: 99%

Unsupervised Single-Generator CycleGAN-Based Pansharpening With Spatial-Spectral Degradation Modeling

Diao,

Jin,

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The study [253] introduces a novel defense mechanism based on adversarial patches that aim to disable the onboard object detection network of the LSST (Low-Slow-Small Target) recognition system by launching an adversarial attack. [254] introduces a novel framework for generating adversarial pan-sharpened images. The proposed method employs a two-stream network to generate the pan-sharpened images and applies shape loss and label loss to carry out the attack task.…”

Section: ① Digital Attackmentioning

confidence: 99%

Benchmarking Adversarial Patch Against Aerial Detection

Lian

Mei

Zhang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Deep neural networks (DNNs) have found widespread applications in interpreting remote sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are vulnerable to different types of noises, particularly adversarial noises. Surprisingly, there has been a lack of comprehensive studies on the robustness of RS tasks, prompting us to undertake a thorough survey and benchmark on the robustness of image classification and object detection in RS. To our best knowledge, this study represents the first comprehensive examination of both natural robustness and adversarial robustness in RS tasks. Specifically, we have curated and made publicly available datasets that contain natural and adversarial noises. These datasets serve as valuable resources for evaluating the robustness of DNNs-based models. To provide a comprehensive assessment of model robustness, we conducted meticulous experiments with numerous different classifiers and detectors, encompassing a wide range of mainstream methods. Through rigorous evaluation, we have uncovered insightful and intriguing findings, which shed light on the relationship between adversarial noise crafting and model training, yielding a deeper understanding of the susceptibility and limitations of various models, and providing guidance for the development of more resilient and robust models

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Adversarial pan-sharpening attacks for object detection in remote sensing

Cited by 18 publications

References 15 publications

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

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

Unsupervised Single-Generator CycleGAN-Based Pansharpening With Spatial-Spectral Degradation Modeling

Benchmarking Adversarial Patch Against Aerial Detection

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