Results 61 to 70 of about 5,561,446 (302)
Adversarial Attacks to Manipulate Target Localization of Object Detector
IEEE AccessAdversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu +7 more
doaj +1 more source
Simple Transparent Adversarial Examples
, 2021 There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different ...
Borkar, Jaydeep, Chen, Pin-Yu
openaire +2 more sources
Journal of Imaging, 2022 Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing.
Wei Zong +4 more
doaj +1 more source
, 2018 It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input.
Athalye Anish +18 more
core +1 more source
Classification score approach for detecting adversarial example in deep neural network
Multimedia tools and applications, 2020 Deep neural networks (DNNs) provide superior performance on machine learning tasks such as image recognition, speech recognition, pattern analysis, and intrusion detection.
Hyun Kwon +3 more
semanticscholar +1 more source
Unauthorized AI cannot recognize me: Reversible adversarial example [PDF]
Pattern Recognition, 2018 In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples.
Jiayang Liu +4 more
semanticscholar +1 more source
Unrestricted Adversarial Examples
, 2018 We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries.
Brown, Tom B. +5 more
openaire +2 more sources
Exploring Adversarial Examples [PDF]
, 2018 Failure cases of black-box deep learning, e.g. adversarial examples, might have severe consequences in healthcare. Yet such failures are mostly studied in the context of real-world images with calibrated attacks. To demystify the adversarial examples, rigorous studies need to be designed.
Kügler, David +3 more
openaire +2 more sources
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
doaj +1 more source
Adversarial Diversity and Hard Positive Generation
, 2016 State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs.
Boult, Terrance E. +2 more
core +1 more source