Results 11 to 20 of about 5,384,791 (287)
DroidEnemy: Battling adversarial example attacks for Android malware detection
Digital Communications and Networks, 2022 In recent years, we have witnessed a surge in mobile devices such as smartphones, tablets, smart watches, etc., most of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular,
Neha Bala +5 more
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Adversarial Sample Detection in Computer Vision:A Survey [PDF]
Jisuanji kexueWith the increase in data volume and improvement in hardware performance,deep learning(DL) has made significant progress in the field of computer vision.However,deep learning models are vulnerable to adversarial samples,causing significant changes in the
ZHANG Xin, ZHANG Han, NIU Manyu, JI Lixia
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Energy-Based Adversarial Example Detection for SAR Images
Remote Sensing, 2022 Adversarial examples (AEs) bring increasing concern on the security of deep-learning-based synthetic aperture radar (SAR) target recognition systems. SAR AEs with perturbation constrained to the vicinity of the target have been recently in the spotlight ...
Zhiwei Zhang +4 more
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Adversarial Example Detection in Deployed Tree Ensembles
CoRR, 2022 Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a misprediction from the model. This can degrade performance and erode a user's trust in the model.
Laurens Devos, Wannes Meert, Jesse Davis
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Adversarial Examples Detection With Bayesian Neural Network [PDF]
IEEE Transactions on Emerging Topics in Computational IntelligenceIn this paper, we propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors and make it easier to simulate the output distribution of a deep neural network. With these observations, we propose a novel Bayesian adversarial example detector, short for BATer, to ...
Yao Li 0015 +3 more
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DOG: An Object Detection Adversarial Attack Method
IEEE AccessThis study presents an object detection adversarial attack method (DOG) based on the dynamic optimization of a multi-scale feature grid cluster, aimed at addressing the challenges of poor transferability in white-box attacks and long generation cycles in
Jinpeng Li +3 more
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Imperceptible Adversarial Examples For Fake Image Detection [PDF]
2021 IEEE International Conference on Image Processing (ICIP), 2021 Accepted by ICIP ...
Quanyu Liao +8 more
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Clustering Approach for Detecting Multiple Types of Adversarial Examples
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
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A Universal Detection Method for Adversarial Examples and Fake Images
Sensors, 2022 Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable
Jiewei Lai +3 more
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Masked Language Model Based Textual Adversarial Example Detection
, 2023 Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown
Zhang, LY +6 more
core +3 more sources