328 citations · 384 across the 11 of their papers we have counts for
11 papers
Do Vision-Language Models Leak What They Learn? Adaptive Token-Weighted Model Inversion Attacks
Ngoc-Bao Nguyen, Sy-Tuyen Ho, Koh Jun Hao +1
Model inversion (MI) attacks pose significant privacy risks by reconstructing private training data from trained neural networks. While prior studies have primarily examined unimod…
Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment
Sy-Tuyen Ho, Koh Jun Hao, Ngoc-Bao Nguyen +2
Model Inversion attacks aim to reconstruct information from private training data by exploiting access to a target model. Nearly all recent MI studies evaluate attack success using…
Synth-Align: Improving Trustworthiness in Vision-Language Model with Synthetic Preference Data Alignment
Robert Wijaya, Ngoc-Bao Nguyen, Ngai-Man Cheung
Large Vision-Language Models (LVLMs) have shown promising capabilities in understanding and generating information by integrating both visual and textual data. However, current mod…
On the Vulnerability of Skip Connections to Model Inversion Attacks
Jun Hao Koh, Sy-Tuyen Ho, Ngoc-Bao Nguyen +1
Skip connections are fundamental architecture designs for modern deep neural networks (DNNs) such as CNNs and ViTs. While they help improve model performance significantly, we iden…
Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?
Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai +4
Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate o…
Model Inversion Robustness: Can Transfer Learning Help?
Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran +2
Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performa…
Label-Only Model Inversion Attacks via Knowledge Transfer
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML) model to infer and reconstruct private training data. Remarkable progress has been made in t…
Re-thinking Model Inversion Attacks Against Deep Neural Networks
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive informa…
On Data Augmentation for GAN Training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +2
Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data in GAN training. Yet it is expensive to collect data in many domains such…
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +2
Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) traini…
An Improved Self-supervised GAN via Adversarial Training
Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen +1
We propose to improve unconditional Generative Adversarial Networks (GAN) by training the self-supervised learning with the adversarial process. In particular, we apply self-superv…