Author name cluster

Guangdong Bai

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

FracFace: Breaking the Visual Clues—Fractal-Based Privacy-Preserving Face Recognition

Wanying Dai
Beibei Li
Naipeng Dong
Guangdong Bai
Jin Song Dong

Face recognition is essential for identity authentication, but the rich visual clues in facial images pose significant privacy risks, highlighting the critical importance of privacy-preserving solutions. For instance, numerous studies have shown that generative models are capable of effectively performing reconstruction attacks that result in the restoration of original visual clues. To mitigate this threat, we introduce FracFace, a fractal-based privacy-preserving face recognition framework. This approach effectively weakens the visual clues that can be exploited by reconstruction attacks by disrupting the spatial structure in frequency domain features, while retaining the vital visual clues required for identity recognition. To achieve this, we craft a Frequency Channels Refining module that reduces sparsity in the frequency domain. It suppresses visual clues that could be exploited by reconstruction attacks, while preserving features indispensable for recognition, thus making these attacks more challenging. More significantly, we design a Frequency Fractal Mapping module that obfuscates deep representations by remapping refined frequency channels into a fractal-based privacy structure. By leveraging the self-similarity of fractals, this module preserves identity relevant features while enhancing defense capabilities, thereby improving the overall robustness of the protection scheme. Experiments conducted on multiple public face recognition benchmarks demonstrate that the proposed FracFace significantly reduces the visual recoverability of facial features, while maintaining high recognition accuracy, as well as the superiorities over state-of-the-art privacy protection approaches.

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ICLR Conference 2025 Conference Paper

GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation

Danny Wang
Ruihong Qiu
Guangdong Bai
Zi Huang

Despite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of the most effective techniques to detect OOD nodes is to expose the detector model with an additional OOD node-set, yet the extra OOD instances are often difficult to obtain in practice. Recent methods for image data address this problem using OOD data synthesis, typically relying on pre-trained generative models like Stable Diffusion. However, these approaches require vast amounts of additional data, as well as one-for-all pre-trained generative models, which are not available for graph data. Therefore, we propose the GOLD framework for graph OOD detection, an implicit adversarial learning pipeline with synthetic OOD exposure without pre-trained models. The implicit adversarial training process employs a novel alternating optimisation framework by training: (1) a latent generative model to regularly imitate the in-distribution (ID) embeddings from an evolving GNN, and (2) a GNN encoder and an OOD detector to accurately classify ID data while increasing the energy divergence between the ID embeddings and the generative model's synthetic embeddings. This novel approach implicitly transforms the synthetic embeddings into pseudo-OOD instances relative to the ID data, effectively simulating exposure to OOD scenarios without auxiliary data. Extensive OOD detection experiments are conducted on five benchmark graph datasets, verifying the superior performance of GOLD without using real OOD data compared with the state-of-the-art OOD exposure and non-exposure baselines.

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ECAI Conference 2023 Conference Paper

SPAT: Semantic-Preserving Adversarial Transformation for Perceptually Similar Adversarial Examples

Subrat Kumar Swain
Vireshwar Kumar
Dan Dongseong Kim
Guangdong Bai

Although machine learning models achieve high classification accuracy against benign examples, they are vulnerable to adversarial machine learning (AML) attacks which generate adversarial examples by adding well-crafted perturbations to the benign examples. The perturbations can be increased to enhance the attack success rate, however, if the perturbations are added without considering the semantic or perceptual similarity between the benign and adversarial examples, the attack can be easily perceived/detected. As such, there exists a trade-off between the attack success rate and the perceptual similarity. In this paper, we propose a novel Semantic-Preserving Adversarial Transformation (SPAT) framework which facilitates an advantageous trade-off between the two metrics. SPAT modifies the optimisation objective of an AML attack to include the goal of increasing the attack success rate as well as the goal of maintaining the perceptual similarity between benign and adversarial examples. Our experiments on a variety of datasets including CIFAR-10, GTSRB, and MNIST demonstrate that SPAT-transformed AML attacks achieve better perceptual similarity while maintaining the attack success rates as the conventional AML attacks.

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IS Journal 2021 Journal Article

Differentially Private Collaborative Coupling Learning for Recommender Systems

Yanjun Zhang
Guangdong Bai
Mingyang Zhong
Xue Li
Ryan K. L. Ko

Coupling learning is designed to estimate, discover, and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand—it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this article, we develop a distributed collaborative coupling learning system, which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favorable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs.

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KER Journal 2020 Journal Article

A blockchain-based decentralized booking system

Naipeng Dong
Guangdong Bai
Lung-Chen Huang
Edmund Kok Heng Lim
Jin Song Dong

Abstract Blockchain technology has rapidly emerged as a decentralized trusted network to replace the traditional centralized intermediator. Especially, the smart contracts that are based on blockchain allow users to define the agreed behaviour among them, the execution of which will be enforced by the smart contracts. Based on this, we propose a decentralized booking system that uses the blockchain as the intermediator between hoteliers and travellers. The system enjoys the trustworthiness of blockchain, improves efficiency and reduces the cost of the traditional booking agencies. The design of the system has been formally modelled using the CSP# language and verified using the model checker Process Analysis Toolkit. We have implemented a prototype decentralized booking system based on the Ethereum ecosystem.

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