Results 41 to 50 of about 124,232 (320)

Learning Universal Adversarial Perturbations with Generative Models [PDF]

, 2018
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification.
Danezis, George, Hayes, Jamie
core   +2 more sources

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

IEEE Access, 2020
Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree ...
Xueqin Zhang, Yue Zhou, Songwen Pei, Jingjing Zhuge, Jiahao Chen   +4 more
doaj   +1 more source

NAG: Network for Adversary Generation [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility constraint to craft the ...
Utkarsh Ojha, R. Venkatesh Babu, Utsav Garg, Konda Reddy Mopuri   +3 more
openaire   +3 more sources

Generative Adversarial Networks: A Primer for Radiologists

RadioGraphics, 2021
Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink, Anirban Mukhopadhyay, Tim Leiner, Thomas J. Vogl, Andreas M. Bucher, Ivana Išgum   +5 more
openaire   +6 more sources

A quantum generative adversarial network for distributions [PDF]

Quantum Machine Intelligence, 2021
AbstractRecent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a ...
Alexei Kondratyev, Amine Assouel, Antoine Jacquier, Antoine Jacquier   +3 more
openaire   +3 more sources

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

SINTECH (Science and Information Technology) Journal, 2023
In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium.
Khairunnisak Khairunnisak, Gilang Miftakhul Fahmi, Didit Suhartono   +2 more
doaj   +1 more source

Sparse Generative Adversarial Network [PDF]

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019
We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the ...
Hamid Krim, Ashkan Panahi, Shahin Mahdizadehaghdam   +2 more
openaire   +3 more sources

Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks

Sensors, 2020
Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc.
Kun Zhou, Wenyong Wang, Teng Hu, Kai Deng   +3 more
doaj   +1 more source

Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images

International Journal of Digital Multimedia Broadcasting, 2021
The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution ...
Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue   +4 more
doaj   +1 more source

Improved Wasserstein conditional generative adversarial network speech enhancement

EURASIP Journal on Wireless Communications and Networking, 2018
The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but performance in the low-data regime and tasks like unseen data learning still lag behind.
Shan Qin, Ting Jiang
doaj   +1 more source