Results 61 to 70 of about 505,656 (201)
Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector
IEEE Access, 2023 Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image.
Xiaochun Lei +5 more
doaj +1 more source
Adversarial Ranking Attack and Defense [PDF]
, 2020 Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou +4 more
openaire +3 more sources
Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
IEEE Access, 2018 Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon +4 more
doaj +1 more source
Aliasing is a Driver of Adversarial Attacks
CoRR, 2022 Aliasing is a highly important concept in signal processing, as careful consideration of resolution changes is essential in ensuring transmission and processing quality of audio, image, and video. Despite this, up until recently aliasing has received very little consideration in Deep Learning, with all common architectures carelessly sub-sampling ...
Adrián Rodríguez-Muñoz +1 more
openaire +3 more sources
Adversarial Attacks against the Perception System of Autonomous Vehicles
, 2023 The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety.
Gao, Yuxing (author)
core
Wasserstein Adversarial Robustness [PDF]
, 2020 Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks.
Wu, Kaiwen
core
Adversarial Risk Análysis for Counterterrorism Modelling [PDF]
, 2013 Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
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AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems [PDF]
, 2023 Evaluating the risk level of adversarial images is essential for safely deploying face authentication models in the real world. Popular approaches for physical-world attacks, such as print or replay attacks, suffer from some limitations, like including ...
Jain, Anil K. +3 more
core +1 more source
Benign Adversarial Attack: Tricking Models for Goodness [PDF]
, 2022 In spite of the successful application in many fields, machine learning models today suffer from notorious problems like vulnerability to adversarial examples.
Lin, Zhiyu +3 more
core +1 more source
Probabilistic Categorical Adversarial Attack & Adversarial Training
, 2023 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive ...
Tang, Jiliang +6 more
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