Results 31 to 40 of about 505,564 (200)
CoRR, 2021 Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model ...
Andjela Mladenovic +6 more
openaire +3 more sources
Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification [PDF]
, 2023 Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks.
Panagiotis Andriotis +8 more
core +1 more source
Scale-Adaptive Adversarial Patch Attack for Remote Sensing Image Aircraft Detection
Remote Sensing, 2021 With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote
Mingming Lu, Qi Li, Li Chen, Haifeng Li
doaj +1 more source
CoRR, 2022 Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Thomas Cilloni +2 more
openaire +2 more sources
Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images
Remote Sensing, 2022 Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack.
Yichuang Zhang +6 more
doaj +1 more source
GenDroid: A query-efficient black-box android adversarial attack framework
, 2023 The security problems of Android applications have been gradually exposed with the increasing popularity of the Android OS. Machine learning (ML) and deep learning (DL) based Android malware detection is still suffering from adversarial attacks, although
Hongfei Shao +17 more
core +1 more source
Meta Gradient Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called
Zheng Yuan 0005 +5 more
openaire +3 more sources
Adv-Eye: A Transfer-Based Natural Eye Makeup Attack on Face Recognition
IEEE Access, 2023 Deep face recognition models are vulnerable to adversarial samples generated by adversarial attack methods. However, current attack methods do not adequately represent the security problems of the deep FR models, because they either produce adversarial ...
Jiatian Pi +6 more
doaj +1 more source
rishitoshsingh/Adversarial-Attack: Alpha
, 2021 No description ...
Rishitosh Kumar Singh
core +1 more source