AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models
IEEE International Conference on Computer Vision, 2023Previous work on adversarial examples typically involves a fixed norm perturbation budget, which fails to capture the way humans perceive perturbations.
Xinquan Chen +4 more
semanticscholar +1 more source
Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems
IEEE Transactions on Cybernetics, 2021Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs).
Jiwei Tian +5 more
semanticscholar +1 more source
MANDA: On Adversarial Example Detection for Network Intrusion Detection System
IEEE Conference on Computer Communications, 2021With the rapid advancement in machine learning (ML), ML-based Intrusion Detection Systems (IDSs) are widely deployed to protect networks from various attacks.
Ning Wang +4 more
semanticscholar +1 more source
Adversarial example detection for DNN models: a review and experimental comparison
Artificial Intelligence Review, 2021Deep learning (DL) has shown great success in many human-related tasks, which has led to its adoption in many computer vision based applications, such as security surveillance systems, autonomous vehicles and healthcare. Such safety-critical applications
Ahmed Aldahdooh +3 more
semanticscholar +1 more source
Adversarial Example Detection Using Latent Neighborhood Graph
IEEE International Conference on Computer Vision, 2021Detection of adversarial examples with high accuracy is critical for the security of deployed deep neural network-based models. We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an ...
Ahmed A. Abusnaina +6 more
semanticscholar +1 more source
Robust Android Malware Detection against Adversarial Example Attacks
The Web Conference, 2021Adversarial examples pose severe threats to Android malware detection because they can render the machine learning based detection systems useless. How to effectively detect Android malware under various adversarial example attacks becomes an essential ...
Heng Li +5 more
semanticscholar +1 more source
Towards Multiple Black-boxes Attack via Adversarial Example Generation Network
ACM Multimedia, 2021The current research on adversarial attacks aims at a single model while the research on attacking multiple models simultaneously is still challenging.
Mingxing Duan +4 more
semanticscholar +1 more source
SmsNet: A New Deep Convolutional Neural Network Model for Adversarial Example Detection
IEEE transactions on multimedia, 2021The emergence of adversarial examples has had a significant impact on the development and application of deep learning. In this paper, a novel convolutional neural network model, the stochastic multifilter statistical network (SmsNet), is proposed for ...
Jinwei Wang +6 more
semanticscholar +1 more source
Adversarial-Example Attacks Toward Android Malware Detection System
IEEE Systems Journal, 2020Recently, it was shown that the generative adversarial network (GAN) based adversarial-example attacks could thoroughly defeat the existing Android malware detection systems.
Heng Li +4 more
semanticscholar +1 more source
Selective Audio Adversarial Example in Evasion Attack on Speech Recognition System
IEEE Transactions on Information Forensics and Security, 2020Deep neural networks (DNNs) are widely used for image recognition, speech recognition, and other pattern analysis tasks. Despite the success of DNNs, these systems can be exploited by what is termed adversarial examples.
Hyun Kwon, Hyun Kwon, H. Yoon, D. Choi
semanticscholar +1 more source