18 citations · 61 across the 51 of their papers we have counts for
11 papers · 1 filter
Reinforcement Unlearning
Dayong Ye, Tianqing Zhu, Congcong Zhu +6
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners. However, one i…
When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
Huan Tian, Guangsheng Zhang, Bo Liu +3
Previous studies have developed fairness methods for biased models that exhibit discriminatory behaviors towards specific subgroups. While these models have shown promise in achiev…
Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang, Xin Shen, Heming Du +10
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images ar…
Generative Adversarial Networks Unlearning
Hui Sun, Tianqing Zhu, Wenhan Chang +1
As machine learning continues to develop, and data misuse scandals become more prevalent, individuals are becoming increasingly concerned about their personal information and are a…
Quantum difference equation for the affine type quiver varieties I: General Construction
Tianqing Zhu
Following the philosophy of \cite{OS22}, we construct quantum difference equations for affine type quiver varieties in terms of the quantum toroidal algebra $U_{q,t}(\hat{\hat{…
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Huiqiang Chen, Tianqing Zhu, Tao Zhang +2
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair res…
Boosting Model Inversion Attacks with Adversarial Examples
Shuai Zhou, Tianqing Zhu, Dayong Ye +2
Model inversion attacks involve reconstructing the training data of a target model, which raises serious privacy concerns for machine learning models. However, these attacks, espec…
Machine Unlearning: A Survey
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision,…
Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation
Chi Liu, Tianqing Zhu, Sheng Shen +1
GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. Although some existing…
Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness
Huajie Chen, Tianqing Zhu, Yuan Zhao +3
Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container…
High-frequency Matters: An Overwriting Attack and defense for Image-processing Neural Network Watermarking
Huajie Chen, Tianqing Zhu, Chi Liu +2
In recent years, there has been significant advancement in the field of model watermarking techniques. However, the protection of image-processing neural networks remains a challen…