Search Results for author: Xinlei He

Found 20 papers, 12 papers with code

You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content

no code implementations10 Aug 2023 Xinlei He, Savvas Zannettou, Yun Shen, Yang Zhang

We find that prompt learning achieves around 10\% improvement in the toxicity classification task compared to the baselines, while for the toxic span detection task we find better performance to the best baseline (0. 643 vs. 0. 640 in terms of $F_1$-score).

Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis

no code implementations13 Jun 2023 Yihan Ma, Zhengyu Zhao, Xinlei He, Zheng Li, Michael Backes, Yang Zhang

In particular, to help the watermark survive the subject-driven synthesis, we incorporate the synthesis process in learning GenWatermark by fine-tuning the detector with synthesized images for a specific subject.

Image Generation

Generated Graph Detection

1 code implementation13 Jun 2023 Yihan Ma, Zhikun Zhang, Ning Yu, Xinlei He, Michael Backes, Yun Shen, Yang Zhang

Graph generative models become increasingly effective for data distribution approximation and data augmentation.

Data Augmentation Face Swapping +1

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

1 code implementation23 May 2023 Yiting Qu, Xinyue Shen, Xinlei He, Michael Backes, Savvas Zannettou, Yang Zhang

Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world.

MGTBench: Benchmarking Machine-Generated Text Detection

1 code implementation26 Mar 2023 Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, Yang Zhang

Nonetheless, we note that only a small fraction of adversarial-crafted perturbations on MGTs can evade the ChatGPT Detector, thus highlighting the need for more robust MGT detection methods.

Benchmarking Question Answering +4

A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots

1 code implementation23 Feb 2023 Boyang Zhang, Xinlei He, Yun Shen, Tianhao Wang, Yang Zhang

Given the simplicity and effectiveness of the attack method, our study indicates scientific plots indeed constitute a valid side channel for model information stealing attacks.

Fine-Tuning Is All You Need to Mitigate Backdoor Attacks

no code implementations18 Dec 2022 Zeyang Sha, Xinlei He, Pascal Berrang, Mathias Humbert, Yang Zhang

Backdoor attacks represent one of the major threats to machine learning models.

On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

2 code implementations13 Dec 2022 Yiting Qu, Xinlei He, Shannon Pierson, Michael Backes, Yang Zhang, Savvas Zannettou

The dissemination of hateful memes online has adverse effects on social media platforms and the real world.

Contrastive Learning

Backdoor Attacks in the Supply Chain of Masked Image Modeling

no code implementations4 Oct 2022 Xinyue Shen, Xinlei He, Zheng Li, Yun Shen, Michael Backes, Yang Zhang

Different from previous work, we are the first to systematically threat modeling on SSL in every phase of the model supply chain, i. e., pre-training, release, and downstream phases.

Contrastive Learning Self-Supervised Learning

Data Poisoning Attacks Against Multimodal Encoders

1 code implementation30 Sep 2022 Ziqing Yang, Xinlei He, Zheng Li, Michael Backes, Mathias Humbert, Pascal Berrang, Yang Zhang

Extensive evaluations on different datasets and model architectures show that all three attacks can achieve significant attack performance while maintaining model utility in both visual and linguistic modalities.

Contrastive Learning Data Poisoning

Auditing Membership Leakages of Multi-Exit Networks

no code implementations23 Aug 2022 Zheng Li, Yiyong Liu, Xinlei He, Ning Yu, Michael Backes, Yang Zhang

Furthermore, we propose a hybrid attack that exploits the exit information to improve the performance of existing attacks.

Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models

no code implementations22 Aug 2022 Xinlei He, Zheng Li, Weilin Xu, Cory Cornelius, Yang Zhang

Finally, we find that data augmentation degrades the performance of existing attacks to a larger extent, and we propose an adaptive attack using augmentation to train shadow and attack models that improve attack performance.

Data Augmentation

Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning

1 code implementation25 Jul 2022 Xinlei He, Hongbin Liu, Neil Zhenqiang Gong, Yang Zhang

The results show that early stopping can mitigate the membership inference attack, but with the cost of model's utility degradation.

Data Augmentation Inference Attack +1

SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders

1 code implementation27 Jan 2022 Tianshuo Cong, Xinlei He, Yang Zhang

Recent research has shown that the machine learning model's copyright is threatened by model stealing attacks, which aim to train a surrogate model to mimic the behavior of a given model.

Self-Supervised Learning

Model Stealing Attacks Against Inductive Graph Neural Networks

1 code implementation15 Dec 2021 Yun Shen, Xinlei He, Yufei Han, Yang Zhang

Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to build powerful applications.

Node-Level Membership Inference Attacks Against Graph Neural Networks

no code implementations10 Feb 2021 Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, Yang Zhang

To fully utilize the information contained in graph data, a new family of machine learning (ML) models, namely graph neural networks (GNNs), has been introduced.

BIG-bench Machine Learning

Quantifying and Mitigating Privacy Risks of Contrastive Learning

1 code implementation8 Feb 2021 Xinlei He, Yang Zhang

Our experimental results show that contrastive models trained on image datasets are less vulnerable to membership inference attacks but more vulnerable to attribute inference attacks compared to supervised models.

BIG-bench Machine Learning Contrastive Learning +3

ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

1 code implementation4 Feb 2021 Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, Yang Zhang

As a result, we lack a comprehensive picture of the risks caused by the attacks, e. g., the different scenarios they can be applied to, the common factors that influence their performance, the relationship among them, or the effectiveness of possible defenses.

BIG-bench Machine Learning Inference Attack +2

Stealing Links from Graph Neural Networks

no code implementations5 May 2020 Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, Yang Zhang

In this work, we propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph.

Fraud Detection Recommendation Systems

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