Search Results for author: Yupei Liu

Found 6 papers, 3 papers with code

Prompt Injection Attacks and Defenses in LLM-Integrated Applications

1 code implementation19 Oct 2023 Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, Neil Zhenqiang Gong

As a result, the literature lacks a systematic understanding of prompt injection attacks and their defenses.

PORE: Provably Robust Recommender Systems against Data Poisoning Attacks

1 code implementation26 Mar 2023 Jinyuan Jia, Yupei Liu, Yuepeng Hu, Neil Zhenqiang Gong

PORE can transform any existing recommender system to be provably robust against any untargeted data poisoning attacks, which aim to reduce the overall performance of a recommender system.

Data Poisoning Recommendation Systems

StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning

no code implementations15 Jan 2022 Yupei Liu, Jinyuan Jia, Hongbin Liu, Neil Zhenqiang Gong

A pre-trained encoder may be deemed confidential because its training requires lots of data and computation resources as well as its public release may facilitate misuse of AI, e. g., for deepfakes generation.

Self-Supervised Learning

BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning

3 code implementations1 Aug 2021 Jinyuan Jia, Yupei Liu, Neil Zhenqiang Gong

In particular, our BadEncoder injects backdoors into a pre-trained image encoder such that the downstream classifiers built based on the backdoored image encoder for different downstream tasks simultaneously inherit the backdoor behavior.

Backdoor Attack Self-Supervised Learning

Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks

no code implementations7 Dec 2020 Jinyuan Jia, Yupei Liu, Xiaoyu Cao, Neil Zhenqiang Gong

Moreover, our evaluation results on MNIST and CIFAR10 show that the intrinsic certified robustness guarantees of kNN and rNN outperform those provided by state-of-the-art certified defenses.

Data Poisoning

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