Results 31 to 40 of about 221,929 (334)
Generating Adversarial Examples with Adversarial Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Xiao, Chaowei +5 more
openaire +2 more sources
Applied Sciences, 2023 Advancements in technology have improved human well-being but also enabled new avenues for criminal activities, including digital exploits like deep fakes, online fraud, and cyberbullying.
Claudio Navar Valdebenito Maturana +2 more
doaj +1 more source
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-
Tao Xu +6 more
semanticscholar +1 more source
An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks
IEEE Internet of Things Journal, 2023 As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns ...
Cheolhee Park +5 more
semanticscholar +1 more source
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed.
Jie Gui +4 more
semanticscholar +1 more source
A PRIMER ON GENERATIVE ADVERSARIAL NETWORKS
International Journal of Innovative Research in Computer Science & Technology, 2020 Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs.
Dr. Vikas Thada +4 more
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A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
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Variational Generative Adversarial Networks for Preventing Mode Collapse [PDF]
هوش محاسباتی در مهندسی برق, 2022 Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by ...
Mehdi Jamaseb Khollari +2 more
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Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019 We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network. In the proposed method, a non-autoregressive WaveNet is trained by jointly optimizing multi-resolution ...
Ryuichi Yamamoto +2 more
semanticscholar +1 more source
Super‐resolution with adversarial loss on the feature maps of the generated high‐resolution image
Electronics Letters, 2022 Recent studies on image super‐resolution make use of Generative Adversarial Networks to generate the high‐resolution image counterpart of the low‐resolution input. However, while being able to generate sharp high‐resolution images, Generative Adversarial
I. Imanuel, S. Lee
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