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Found 6 papers, 0 papers with code
Intriguing Properties of Diffusion Models: A Large-Scale Dataset for Evaluating Natural Attack Capability in Text-to-Image Generative Models
Motivated by the finding, we construct a large-scale dataset, Natural Denoising Diffusion Attack (NDDA) dataset, to systematically evaluate the risk of the natural attack capability of diffusion models with state-of-the-art text-to-image diffusion models.
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack
In this work, we conduct the first measurement study on whether and how effectively the existing designs can lead to system-level effects, especially for the STOP sign-evasion attacks due to their popularity and severity.
SoK: On the Semantic AI Security in Autonomous Driving
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack
Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical.
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack
Lane-Keeping Assistance System (LKAS) is convenient and widely available today, but also extremely security and safety critical.
Interpretable Deep Learning under Fire
The improved interpretability is believed to offer a sense of security by involving human in the decision-making process.
