Results 61 to 70 of about 5,389,393 (319)
Unrestricted Adversarial Examples
CoRR, 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.
Tom B. Brown +5 more
openaire +2 more sources
, 2021 Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers. State-of-the-art adversarial example detection methods characterize an input example as adversarial either by quantifying the magnitude of ...
Arora, Sunpreet S +3 more
core +1 more source
IEEE Access, 2020 Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu +4 more
doaj +1 more source
Exploring Diverse Feature Extractions for Adversarial Audio Detection
IEEE Access, 2023 Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi +3 more
doaj +1 more source
Example of false adversarial sample.
, 2022 We present an example of a false adversarial sample (right) and its respective original sample (left). The false adversarial sample is built with few total perturbations (i.e., low L1 and L2) but an unrecognisable final image (false adversarial sample ...
Danilo Vasconcellos Vargas (12418891) +1 more
core +1 more source
Adversarial Examples for Generative Models [PDF]
2018 IEEE Security and Privacy Workshops (SPW), 2018 We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks.
Jernej Kos, Ian Fischer, Dawn Song
openaire +2 more sources
Adversarial examples in remote sensing [PDF]
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018 This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet. In particular, we present a new study of adversarial examples in the context of satellite image classification
Wojciech Czaja +4 more
openaire +2 more sources
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing
Frontiers in Artificial Intelligence, 2022 Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. In this paper, we develop improved techniques for defending
Xingjian Li +4 more
doaj +1 more source
Ensemble Adversarial Example Defense Based on Generative Adversarial Network
工程科学与技术, 2022 Given the bottlenecks of existing adversarial example defense schemes, such as insufficient defense capability and high time consumption, an ensemble adversarial example defense scheme based on the generative adversarial network was proposed in this ...
Tianjie CAO +5 more
doaj
Escaping Filter-based Adversarial Example Defense: A Reinforcement Learning Approach
, 2022 An adversarial example is a specially-crafted example with subtle and intentional perturbations that causes a machine learning model to make a false classification.
Li, Yantao +5 more
core +1 more source