A Survey on Physical Adversarial Attack in Computer Vision

Wang, Donghua; Wen, Yuh‐Sheng; Jiang, Tingsong; Tang, Guijian; Chen, Xiaoqian

doi:10.48550/arxiv.2209.14262

Cited by 5 publications

(4 citation statements)

References 92 publications

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“…For example, Wei et al [14] focus on a subset of 41 physical adversarial attacks, while Fang et al [15] specifically address optical-based physical adversarial attacks. Similarly, Wang et al [16] primarily survey the tasks of image recognition and object detection, with some coverage of physical adversarial attacks in object tracking and semantic segmentation. However, their coverage is limited to a total of 71 attacks, with 38 attacks related to image recognition and 33 attacks related to object detection.…”

Section: Introductionmentioning

confidence: 99%

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Guesmi,

Hanif,

Ouni

et al. 2023

IEEE Access

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Deep Neural Networks (DNNs) have shown impressive performance in computer vision tasks; however, their vulnerability to adversarial attacks raises concerns regarding their security and reliability. Extensive research has shown that DNNs can be compromised by carefully crafted perturbations, leading to significant performance degradation in both digital and physical domains. Therefore, ensuring the security of DNN-based systems is crucial, particularly in safety-critical domains such as autonomous driving, robotics, smart homes/cities, smart industries, video surveillance, and healthcare. In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Furthermore, we explore the specific requirements and challenges associated with executing attacks in the physical world. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications, including classification, detection, face recognition, semantic segmentation and depth estimation. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness. We examine how each technique strives to ensure the successful manipulation of DNNs while mitigating the risk of detection and withstanding real-world distortions. Lastly, we discuss the current challenges and outline potential future research directions in the field of physical adversarial attacks. We highlight the need for enhanced defense mechanisms, the exploration of novel attack strategies, the evaluation of attacks in different application domains, and the establishment of standardized benchmarks and evaluation criteria for physical adversarial attacks. Through this comprehensive survey, we aim to provide a valuable resource for researchers, practitioners, and policymakers to gain a holistic understanding of physical adversarial attacks in computer vision and facilitate the development of robust and secure DNN-based systems.

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Section: Introductionmentioning

confidence: 99%

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Guesmi,

Hanif,

Ouni

et al. 2023

IEEE Access

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“…To comprehensively assess the robustness of these models, we conducted rigorous experiments involving a diverse set of classifiers and detectors, representing a wide range of mainstream methods. Through this extensive evaluation, we have uncovered insightful and intriguing findings that illuminate the relationship between the crafting of [32] IEEE Access ✓ ✕ ✕ ✕ ✕ ✕ 2018 [34] Computer Science Review ✓ ✕ ✕ ✕ ✕ ✕ 2018 [31] arXiv ✓ ✕ ✕ ✕ ✕ ✕ 2019 [33] Applied Science ✓ ✕ ✕ ✕ ✕ ✕ 2020 [42] ACM Computing Surveys ✓ ✕ ✕ ✕ ✕ ✕ 2021 [35] IEEE Access ✓ ✕ ✕ ✕ ✕ ✕ 2021 [41] ACM Computing Surveys ✓ ✕ ✕ ✕ ✕ ✕ 2021 [40] TII ✓ ✕ ✕ ✕ ✕ ✕ 2022 [47] arXiv ✓ * ✕ ✕ ✕ ✕ 2022 [48] INJOIT ✓ ✕ ✕ ✕ ✕ ✕ 2022 [49] Artificial Intelligence Review ✓ ✕ ✓ ✕ ✓ ✕ 2022 [39] TPAMI ✓ ✕ ✕ ✕ ✕ ✕ 2022 [38] TII ✕ ✕ ✕ ✕ ✕ ✕ 2022 [49] arXiv ✓ * ✕ ✕ ✕ ✕ 2022 [25] arXiv ✓ ✕ ✕ ✕ ✕ ✕ 2022 [44] arXiv ✓ * ✕ ✕ ✕ ✕ 2022 [45] arXiv * ✓ ✕ ✕ ✕ ✕ 2022 [37] Neurocomputing ✓ ✕ ✕ ✕ ✕ ✕ 2023 [50] ACM Computing Surveys * ✕ ✕ ✕ ✕ ✕ 2023 [28] arXiv ✓ ✕ ✕ ✕ ✕ ✕ 2023 [46] ICAI * ✓ ✕ ✕ ✕ ✕ Benchmarks 2020 [29] CVPR ✕ ✕ ✓ ✕ ✓ ✕ 2021 [27] arXiv ✕ ✕ ✓ ✕ ✓ ✓ 2022 [51] IJCAI ✕ ✕ ✕ ✓ ✓ ✕ 2022 [26] NIPS ✕ ✕ ✓ ✕ ✓ ✕ 2022 [52] arXiv ✕ ✕ ✕ ✓ ✓ ✕ 2022 [36] Pattern Recognition ✕ ✕ ✓ ✕ ✓ ✕ 2023 [30] arXiv ✕ ✕ ✓ ✕ ✓ ✓ 2023 [53] Pattern Recognition ✕ ✕ ✓ ✕ ✓ ✕ 2023 [54] CVPR…”

Section: Introductionmentioning

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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“…The evaluation of explainability of DNN models is known to be a challenging task, necessitating such an effort. From another perspective, while there have been many surveys of literature on adversarial attacks and robustness [7,8,11,25,29,35,46,51,57,61,65,69,75,77,101,104,112,113,116,118,119,121,122,129,135] -which focus on attacking the predictive outcome of these models, there have been no effort so far to study and consolidate existing efforts on attacks on explainability of DNN models. Many recent efforts have demonstrated the vulnerability of explanations (or attributions 1 ) to human-imperceptible input perturbations across image, text and tabular data [36,45,55,62,107,108,133].…”

Section: Introductionmentioning

confidence: 99%

On the Robustness of Explanations of Deep Neural Network Models: A Survey

Jyoti¹,

Ganesh²,

Gayala³

et al. 2022

Preprint

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Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

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A Survey on Physical Adversarial Attack in Computer Vision

Cited by 5 publications

References 92 publications

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Benchmarking Adversarial Patch Against Aerial Detection

On the Robustness of Explanations of Deep Neural Network Models: A Survey

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