Author name cluster

Guozhu Meng

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3 papers

2 author rows

ECAI Conference 2025 Conference Paper

AUTHNET: Neural Network with Integrated Authentication Logic

Yuling Cai
Fan Xiang
Guozhu Meng
Yinzhi Cao
Kai Chen 0012

Model stealing, i. e. , unauthorized access and exfiltration of deep learning models, has emerged as a significant security threat. The misuse and illegal replication of models pose major risks to financial assets and competitive advantage. Traditional protection methods, such as model watermarking, are passive and challenging to enforce, while active defenses often face limitations in terms of efficiency and the security required for widespread deployment. To this end, we propose a native authentication mechanism, called AUTHNET, which integrates authentication logic as part of the model without any additional structures. Our key insight is to reuse redundant neurons with low activation and embed authentication bits in an intermediate layer, called a gate layer. Then, AUTHNET fine-tunes the layers after the gate layer to embed authentication logic so that only inputs with secret key can trigger the correct logic of AUTHNET. It provides the last line of defense, i. e. , even being exfiltrated, the model is not usable as the adversary cannot generate valid inputs without the key. We theoretically demonstrate the high sensitivity of AUTHNET to the secret key, which means that precise key provision is essential for achieving good performance of AUTHNET. AUTHNET is compatible with any convolutional neural network, where our extensive evaluations show that AUTHNET successfully achieves the goal in rejecting unauthenticated users (whose average accuracy drops to 22. 03%) with a trivial accuracy decrease (1. 18% on average) for legitimate users, and is robust against adaptive attacks, providing efficient and lightweight protection.

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AAAI Conference 2024 Conference Paper

DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models

Jiachen Zhou
Peizhuo Lv
Yibing Lan
Guozhu Meng
Kai Chen
Hualong Ma

Dataset sanitization is a widely adopted proactive defense against poisoning-based backdoor attacks, aimed at filtering out and removing poisoned samples from training datasets. However, existing methods have shown limited efficacy in countering the ever-evolving trigger functions, and often leading to considerable degradation of benign accuracy. In this paper, we propose DataElixir, a novel sanitization approach tailored to purify poisoned datasets. We leverage diffusion models to eliminate trigger features and restore benign features, thereby turning the poisoned samples into benign ones. Specifically, with multiple iterations of the forward and reverse process, we extract intermediary images and their predicted labels for each sample in the original dataset. Then, we identify anomalous samples in terms of the presence of label transition of the intermediary images, detect the target label by quantifying distribution discrepancy, select their purified images considering pixel and feature distance, and determine their ground-truth labels by training a benign model. Experiments conducted on 9 popular attacks demonstrates that DataElixir effectively mitigates various complex attacks while exerting minimal impact on benign accuracy, surpassing the performance of baseline defense methods.

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IJCAI Conference 2023 Conference Paper

Fairness via Group Contribution Matching

Tianlin Li
Zhiming Li
Anran Li
Mengnan Du
Aishan Liu
Qing Guo
Guozhu Meng
Yang Liu

Fairness issues in Deep Learning models have recently received increasing attention due to their significant societal impact. Although methods for mitigating unfairness are constantly proposed, little research has been conducted to understand how discrimination and bias develop during the standard training process. In this study, we propose analyzing the contribution of each subgroup (i. e. , a group of data with the same sensitive attribute) in the training process to understand the cause of such bias development process. We propose a gradient-based metric to assess training subgroup contribution disparity, showing that unequal contributions from different subgroups are one source of such unfairness. One way to balance the contribution of each subgroup is through oversampling, which ensures that an equal number of samples are drawn from each subgroup during each training iteration. However, we have found that even with a balanced number of samples, the contribution of each group remains unequal, resulting in unfairness under the oversampling strategy. To address the above issues, we propose an easy but effective group contribution matching (GCM) method to match the contribution of each subgroup. Our experiments show that our GCM effectively improves fairness and outperforms other methods significantly.

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