Dual-Mode Method for Generating Adversarial Examples to Attack Deep Neural Networks

Kwon, Hyun; Kim, Sunghwan

doi:10.1109/access.2023.3245632

Cited by 4 publications

(1 citation statement)

References 21 publications

(22 reference statements)

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“…With the rapid development of deep neural networks, breakthroughs have been made in text, image and speech processing [34][35][36][37]. As a crucial task in the field of computer vision, object detection has attracted a lot of research attention.…”

Section: Related Workmentioning

confidence: 99%

Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

Zhou

et al. 2023

Remote Sensing

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With the rapid development of artificial intelligence and computer vision, deep learning has become widely used for aircraft detection. However, aircraft detection is still a challenging task due to the small target size and dense arrangement of aircraft and the complex backgrounds in remote sensing images. Existing remote sensing aircraft detection methods were mainly designed based on algorithms employed in general object detection methods. However, these methods either tend to ignore the key structure and size information of aircraft targets or have poor detection effects on densely distributed aircraft targets. In this paper, we propose a novel multi-task aircraft detection algorithm. Firstly, a multi-task joint training method is proposed, which provides richer semantic structure features for bounding box localization through landmark detection. Secondly, a multi-task inference algorithm is introduced that utilizes landmarks to provide additional supervision for bounding box NMS (non-maximum suppression) filtering, effectively reducing false positives. Finally, a novel loss function is proposed as a constrained optimization between bounding boxes and landmarks, which further improves aircraft detection accuracy. Experiments on the UCAS-AOD dataset demonstrated the state-of-the-art precision and efficiency of our proposed method compared to existing approaches. Furthermore, our ablation study revealed that the incorporation of our designed modules could significantly enhance network performance.

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Section: Related Workmentioning

confidence: 99%

Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

Zhou

et al. 2023

Remote Sensing

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MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions

Li,

Wei,

2024

Applied Sciences

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In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity.

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Reflective Adversarial Attacks against Pedestrian Detection Systems for Vehicles at Night

Chen,

Wu,

Cui

et al. 2024

Symmetry

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The advancements in deep learning have significantly enhanced the accuracy and robustness of pedestrian detection. However, recent studies reveal that adversarial attacks can exploit the vulnerabilities of deep learning models to mislead detection systems. These attacks are effective not only in digital environments but also pose significant threats to the reliability of pedestrian detection systems in the physical world. Existing adversarial attacks targeting pedestrian detection primarily focus on daytime scenarios and are easily noticeable by road observers. In this paper, we propose a novel adversarial attack method against vehicle–pedestrian detection systems at night. Our approach utilizes reflective optical materials that can effectively reflect light back to its source. We optimize the placement of these reflective patches using the particle swarm optimization (PSO) algorithm and deploy patches that blend with the color of pedestrian clothing in real-world scenarios. These patches remain inconspicuous during the day or under low-light conditions, but at night, the reflected light from vehicle headlights effectively disrupts the vehicle’s pedestrian detection systems. Considering that real-world detection models are often black-box systems, we propose a “symmetry” strategy, which involves using the behavior of an alternative model to simulate the response of the target model to adversarial patches. We generate adversarial examples using YOLOv5 and apply our attack to various types of pedestrian detection models. Experiments demonstrate that our approach is both effective and broadly applicable.

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Dual-Mode Method for Generating Adversarial Examples to Attack Deep Neural Networks

Cited by 4 publications

References 21 publications

Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions

Reflective Adversarial Attacks against Pedestrian Detection Systems for Vehicles at Night

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