TransPatch: A Transformer-based Generator for Accelerating Transferable Patch Generation in Adversarial Attacks Against Object Detection Models

Wang, Jinghao; Cui, Chenling; Wen, Xin; Shi, Jie

doi:10.1007/978-3-031-25056-9_21

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…In [192], the authors introduce the Differentiable Transformation Attack (DTA), which enables the creation of patterns that can effectively hide the object from detection, while also taking into account the impact of various transformations that the object may undergo. Wang et al [193] introduce a novel training pipeline called TransPatch to optimize the training efficiency of adversarial patches. To avoid generating conspicuous and attention-grabbing patterns, [160] propose to create physical adversarial patches by leveraging the image manifold of a pre-trained GAN.…”

Section: Black-box Attacksmentioning

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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Section: Black-box Attacksmentioning

confidence: 99%

Benchmarking Adversarial Patch Against Aerial Detection

Lian

Mei

Zhang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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TransNoise: Transferable Universal Adversarial Noise for Adversarial Attack

Wei,

Gao,

Wang

et al. 2023

Lecture Notes in Computer Science

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A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking

Mei,

Lian,

Wang

et al. 2024

J Remote Sens

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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 susceptible and 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 DNNs in RS. This manuscript conducts a comprehensive study of both the natural robustness and adversarial robustness of DNNs in RS tasks. Specifically, we systematically and extensively survey the robustness of DNNs from various perspectives such as noise type, attack domain, and attacker’s knowledge, encompassing typical applications such as object detection and image classification. Building upon this foundation, we further develop a rigorous benchmark for testing the robustness of DNN-based models, which entails the construction of noised datasets, robustness testing, and evaluation. Under the proposed benchmark, we perform a meticulous and systematic examination of the robustness of typical deep learning algorithms in the context of object detection and image classification applications. Through comprehensive survey and benchmark, we uncover 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 DNN-based models, and providing guidance for the development of more resilient and robust models.

show abstract

TransPatch: A Transformer-based Generator for Accelerating Transferable Patch Generation in Adversarial Attacks Against Object Detection Models

Cited by 3 publications

References 37 publications

Benchmarking Adversarial Patch Against Aerial Detection

Benchmarking Adversarial Patch Against Aerial Detection

TransNoise: Transferable Universal Adversarial Noise for Adversarial Attack

A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking

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