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Takami Sato

7 accepted papers

2025

Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-Based Decision-Making Systems

ICLR 2025poster

Large Language Models (LLMs) have shown significant promise in real-world decision-making tasks for embodied artificial intelligence, especially when fine-tuned to leverage their inherent common sense and reasoning abilities while being tailored to specific applications. However, this fine-tuning pr…

Cited by 5SourcePDFScholar
2025

Slamspoof: Practical Lidar Spoofing Attacks on Localization Systems Guided by Scan Matching Vulnerability Analysis

ICRA 2025

Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizing lane shapes, traffic light locations, and traffic signs. Achieving this level of reliance on map information requires

Cited by 3SourcecodeScholar
2024

Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models

CVPR 2024poster

Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs. This high image-generation capability has stimulated the creation of many downstream applications in various areas. Ho…

Cited by 2SourcePDFScholar
2023

Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack

ICCV 2023poster

In autonomous driving (AD), accurate perception is indispensable to achieving safe and secure driving. Due to its safety-criticality, the security of AD perception has been widely studied. Among different attacks on AD perception, the physical adversarial object evasion attacks are especially severe…

Cited by 39PDFScholar
2023

Learning Representation for Anomaly Detection of Vehicle Trajectories

IROS 2023poster

Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations are introduced to those history trajectories, the resulting anomalous (or adversarial) trajectories can significantly mis…

Cited by 24SourceScholar
2023

Semi-supervised Semantics-guided Adversarial Training for Robust Trajectory Prediction

ICCV 2023poster

Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and many other autonomous systems. Recent works demonstrate that adversarial attacks on trajectory prediction, where small crafted perturbations are introduced to history trajectories, may significantly m…

Cited by 21PDFcodeScholar