Physical adversarial attack meets computer vision: A decade survey
Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …
Slowtrack: Increasing the latency of camera-based perception in autonomous driving using adversarial examples
In Autonomous Driving (AD), real-time perception is a critical component responsible for
detecting surrounding objects to ensure safe driving. While researchers have extensively …
detecting surrounding objects to ensure safe driving. While researchers have extensively …
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models
Denoising probabilistic diffusion models have shown breakthrough performance to generate
more photo-realistic images or human-level illustrations than the prior models such as …
more photo-realistic images or human-level illustrations than the prior models such as …
Intriguing properties of diffusion models: A large-scale dataset for evaluating natural attack capability in text-to-image generative models
Denoising probabilistic diffusion models have shown breakthrough performance that can
generate more photo-realistic images or human-level illustrations than the prior models such …
generate more photo-realistic images or human-level illustrations than the prior models such …
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving
Recent research in adversarial machine learning has focused on visual perception in
Autonomous Driving (AD) and has shown that printed adversarial patches can attack object …
Autonomous Driving (AD) and has shown that printed adversarial patches can attack object …
SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving
Autonomous Driving (AD) systems critically depend on visual perception for real-time object
detection and multiple object tracking (MOT) to ensure safe driving. However, high latency in …
detection and multiple object tracking (MOT) to ensure safe driving. However, high latency in …
ADBA: Approximation Decision Boundary Approach for Black-Box Adversarial Attacks
F Wang, X Zuo, H Huang, G Chen - arXiv preprint arXiv:2406.04998, 2024 - arxiv.org
Many machine learning models are susceptible to adversarial attacks, with decision-based
black-box attacks representing the most critical threat in real-world applications. These …
black-box attacks representing the most critical threat in real-world applications. These …
Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder
Autonomous driving presents a complex challenge, which is usually addressed with artificial
intelligence models that are end-to-end or modular in nature. Within the landscape of …
intelligence models that are end-to-end or modular in nature. Within the landscape of …
I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectors
Z Lin, Y Zhao, K Chen, J He - arXiv preprint arXiv:2406.10285, 2024 - arxiv.org
Deep neural networks (DNNs) have revolutionized the field of computer vision like object
detection with their unparalleled performance. However, existing research has shown that …
detection with their unparalleled performance. However, existing research has shown that …
Indirect Gradient Matching for Adversarial Robust Distillation
H Lee, S Cho, C Kim - arXiv preprint arXiv:2312.03286, 2023 - arxiv.org
Adversarial training significantly improves adversarial robustness, but superior performance
is primarily attained with large models. This substantial performance gap for smaller models …
is primarily attained with large models. This substantial performance gap for smaller models …