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Peijin Guo

4 accepted papers

2026

Dual-View Inference Attack: Machine Unlearning Amplifies Privacy Exposure

AAAI 2026technical

Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects the rights of users requesting unlearning, it also introduces new privacy risks. Prior works have primarily focused on th

Cited by 0SourcePDFScholar
2025

AdvEDM: Fine-grained Adversarial Attack against VLM-based Embodied Agents

NeurIPS 2025poster

Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks in embodied agents, such as autonomous driving and robotic manipulation. Recent research has increasingly explored adversarial attacks on VLMs to reveal their…

Cited by 0SourceScholar
2025

An Efficient Residual-based Low-dose PET Reconstruction with Spatial-Frequency Integration

ICASSP 2025accepted

Positron emission tomography (PET) is a nuclear medical imaging technique where image quality depends on the dose of radionuclides administered to the patient. While standard-dose PET (SPET) offers high-quality imaging, it also poses radiation risks. If reconstructing low-dose PET (LPET) images can…

Cited by 0SourceScholar
2025

BadRobot: Jailbreaking Embodied LLM Agents in the Physical World

ICLR 2025poster

Embodied AI represents systems where AI is integrated into physical entities. Multimodal Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue re…

Cited by 0SourcePDFScholar