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Found 25 papers, 1 papers with code
Reinforcement Unlearning
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.
When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers through Membership Inference Attacks
It leverages the difference in the predictions from both the original and fairness-enhanced models and exploits the observed prediction gaps as attack clues.
Generative Adversarial Networks Unlearning
Based on the substitution mechanism and fake label, we propose a cascaded unlearning approach for both item and class unlearning within GAN models, in which the unlearning and learning processes run in a cascaded manner.
Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms
Robust audio anti-spoofing has been increasingly challenging due to the recent advancements on deepfake techniques.
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Federated learning (FL) has been a hot topic in recent years.
Boosting Model Inversion Attacks with Adversarial Examples
Hence, in this paper, we propose a new training paradigm for a learning-based model inversion attack that can achieve higher attack accuracy in a black-box setting.
Machine Unlearning: A Survey
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, speech recognition, medical diagnosis, and more.
Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation
GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes.
Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness
By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images.
New Challenges in Reinforcement Learning: A Survey of Security and Privacy
A large number of studies have focused on these security and privacy problems in reinforcement learning.
How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale.
Momentum Gradient Descent Federated Learning with Local Differential Privacy
The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed.
Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack
To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.
Label-only Model Inversion Attack: The Attack that Requires the Least Information
In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i. e., black-box attacks, or the parameters of a target model, i. e., white-box attacks.
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
However, they may not mean that transfer learning models are impervious to model inversion attacks.
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy
The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
DP-Image: Differential Privacy for Image Data in Feature Space
The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public.
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server.
Distributed, Parallel, and Cluster Computing
Can Steering Wheel Detect Your Driving Fatigue?
In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel.
Correlated Differential Privacy: Feature Selection in Machine Learning
In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover, the privacy issue of data correlation in learning is guaranteed.
Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.
Fairness Constraints in Semi-supervised Learning
Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
Differentially Private Multi-Agent Planning for Logistic-like Problems
To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems.
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Artificial Intelligence (AI) has attracted a great deal of attention in recent years.
A Study of Data Pre-processing Techniques for Imbalanced Biomedical Data Classification
However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets.
