GANDaLF: GAN for Data-Limited Fingerprinting

2021 ◽  
Vol 2021 (2) ◽  
pp. 305-322
Author(s):  
Se Eun Oh ◽  
Nate Mathews ◽  
Mohammad Saidur Rahman ◽  
Matthew Wright ◽  
Nicholas Hopper
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Large Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Closed World ◽  
Training Samples ◽  
A Site

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

Linear electromagnetic inverse scattering via generative adversarial networks

2021 ◽  
pp. 1-9
Author(s):  
Huilin Zhou ◽  
Huimin Zheng ◽  
Qiegen Liu ◽  
Jian Liu ◽  
Yuhao Wang
Keyword(s):  
Inverse Scattering ◽  
Optimization Methods ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Scattering Problems ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Highly Nonlinear ◽  
Electromagnetic Inverse Scattering

Abstract Electromagnetic inverse-scattering problems (ISPs) are concerned with determining the properties of an unknown object using measured scattered fields. ISPs are often highly nonlinear, causing the problem to be very difficult to address. In addition, the reconstruction images of different optimization methods are distorted which leads to inaccurate reconstruction results. To alleviate these issues, we propose a new linear model solution of generative adversarial network-based (LM-GAN) inspired by generative adversarial networks (GAN). Two sub-networks are trained alternately in the adversarial framework. A linear deep iterative network as a generative network captures the spatial distribution of the data, and a discriminative network estimates the probability of a sample from the training data. Numerical results validate that LM-GAN has admirable fidelity and accuracy when reconstructing complex scatterers.

scholarly journals INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 573-582 ◽  
Author(s):  
S. M. Tilon ◽  
F. Nex ◽  
D. Duarte ◽  
N. Kerle ◽  
G. Vosselman
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Essential Information ◽  
Urban Scenes ◽  
Adversarial Network ◽  
Collapsed Buildings ◽  
Post Disaster

Abstract. Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.

scholarly journals A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3269 ◽  
Author(s):  
Hongmin Gao ◽  
Dan Yao ◽  
Mingxia Wang ◽  
Chenming Li ◽  
Haiyun Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
The One ◽  
Hyperspectral Remote Sensing Images

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

scholarly journals Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Guangcheng Bao ◽  
Bin Yan ◽  
Li Tong ◽  
Jun Shu ◽  
Linyuan Wang ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Data Augmentation ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Training Samples ◽  
Using Data ◽  
Effective Models

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of training samples, which makes it difficult to establish effective models for emotion recognition. Inspired by the excellent achievements of generative models in image processing, we propose a data augmentation model named VAE-D2GAN for EEG-based emotion recognition using a generative adversarial network. EEG features representing different emotions are extracted as topological maps of differential entropy (DE) under five classical frequency bands. The proposed model is designed to learn the distributions of these features for real EEG signals and generate artificial samples for training. The variational auto-encoder (VAE) architecture can learn the spatial distribution of the actual data through a latent vector, and is introduced into the dual discriminator GAN to improve the diversity of the generated artificial samples. To evaluate the performance of this model, we conduct a systematic test on two public emotion EEG datasets, the SEED and the SEED-IV. The obtained recognition accuracy of the method using data augmentation shows as 92.5 and 82.3%, respectively, on the SEED and SEED-IV datasets, which is 1.5 and 3.5% higher than that of methods without using data augmentation. The experimental results show that the artificial samples generated by our model can effectively enhance the performance of the EEG-based emotion recognition.

scholarly journals Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kazuma Kokomoto ◽  
Rena Okawa ◽  
Kazuhiko Nakano ◽  
Kazunori Nozaki
Keyword(s):  
Private Information ◽  
Data Augmentation ◽  
Wasserstein Distance ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Pediatric Dentists ◽  
Clinical Cases

AbstractDentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible to utilize intraoral images, progressive growing of generative adversarial networks are used to generate intraoral images. A total of 35,254 intraoral images were used as training data with resolutions of 128 × 128, 256 × 256, 512 × 512, and 1024 × 1024. The results of the training datasets with and without data augmentation were compared. The Sliced Wasserstein Distance was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. 12 pediatric dentists were asked to observe these images and assess whether they were real or generated. The d prime of the 1024 × 1024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512 × 512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that the generated images can be used in dental education or data augmentation for deep learning, without privacy restrictions.

scholarly journals Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 734 ◽  
Author(s):  
Yan Ma ◽  
Kang Liu ◽  
Zhibin Guan ◽  
Xinkai Xu ◽  
Xu Qian ◽  
...  
Keyword(s):  
Object Recognition ◽  
Synthesis Method ◽  
Interactive Systems ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Augmented Reality (AR) is crucial for immersive Human–Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry . Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems.

RESEARCH OF GENERATIVE ADVERSARIAL NETWORKS FOR THE SYNTHESIS OF NEW MEDICAL DATA

2020 ◽  
Vol 2020 (2) ◽  
pp. 17-23
Author(s):  
Vladislav Laptev ◽  
Vyacheslav Danilov ◽  
Olga Gerget
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Medical Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Training Procedure ◽  
Neural Network Architecture ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

The paper considers the development of a Generative Adversarial Network (GAN) for the synthesis of new medical data. The developed GAN consists of two models trained simultaneously: a generative model (G - Generator), estimating the distribution of data, and a discriminating model (D - Discriminator), which estimates the probability that the sample is obtained from the training data, and not from generator G. To create G, we used own neural network architecture based on convolutional layers using experimental functions of Tensor Flow Addons. To create discriminator D, we used a Transfer Learning (TL) approach. The training procedure is to maximize the likelihood that discriminator D will make a mistake. Experiments show that the proposed GAN architecture completely copes with the task of synthesizing of new medical data.

scholarly journals Generative Adversarial Networks

2021 ◽  
Vol 9 (8) ◽  
pp. 2307-2325
Author(s):  
Amey Thakur
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Video Processing ◽  
Super Resolution ◽  
Image Synthesis ◽  
Background Information ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples. Keywords: Generative Adversarial Networks (GANs), Generator, Discriminator, Supervised and Unsupervised Learning, Discriminative and Generative Modelling, Backpropagation, Loss Functions, Machine Learning, Deep Learning, Neural Networks, Convolutional Neural Network (CNN), Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Information Maximizing GAN (InfoGAN), Stacked GAN (StackGAN), Pix2Pix, Wasserstein GAN (WGAN), Progressive Growing GAN (ProGAN), BigGAN, StyleGAN, CycleGAN, Super-Resolution GAN (SRGAN), Image Synthesis, Image-to-Image Translation.

scholarly journals Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Pengfei Zhang ◽  
Xiaoming Ju
Keyword(s):  
Neural Network ◽  
Gaussian Mixture ◽  
Physical World ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Testing Stage

It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.

Systematic Review on glaucoma detection using generative adversarial networks: Coherent Taxonomy, Motivations, Open Challenges, Recommendations and New Research Direction in the emerging Covid19 pandemic (Preprint)

2021 ◽  
Author(s):  
Ali Q. Saeed ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Jemaima Che-Hamzah ◽  
Ahmad Tarmizi Abdul Ghani
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Visual Fields ◽  
Retinal Disease ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Learning Techniques

BACKGROUND Glaucoma means irreversible blindness. Globally, it is the second retinal disease leading to blindness, just preceded by the cataract. Therefore, there is a great need to avoid the silent growth of such disease using the recently developed Generative Adversarial Networks(GANs). OBJECTIVE This paper aims to introduce GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome in order to implement this kind of technology. METHODS To organize this review comprehensively, we used the keywords: ("Glaucoma", "optic disc", "blood vessels") and ("receptive field", "loss function", "GAN", "Generative Adversarial Network", "Deep learning", "CNN", "convolutional neural network" OR encoder), in different variations to gather all the relevant articles from five highly reputed databases: IEEE Xplore, Web of Science, Scopus, Science Direct, and Pubmed. These libraries broadly cover technical and medical literature. For the latest five years of publications, we only included those within that period. Researchers who used OCT or visual fields in their work were excluded. However, papers that used 2D images were included. A large-scale systematic analysis was performed, then a summary was generated. The study was conducted between March 2020 and November 2020. RESULTS We found 59 articles after a comprehensive survey of the literature. Among 59 articles, 29 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. Twenty-nine journal articles discuss recent advances in generative adversarial networks, practical experiments, and analytical studies of retinal disease. CONCLUSIONS Recent deep learning technique, namely generative adversarial network, has shown encouraging retinal disease detection performance. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. There is no existing systematic review paper on retinal disease utilizing generative adversarial networks to the extent of our knowledge. Two paper sets were reported; the first involves surveys on the recent development of GANs or overviews of papers reported in the literature applying machine learning techniques on retinal diseases. While in the second group, researchers have sought to establish and enhance the detection process through generating as real as possible synthetic images with the assistance of GANs. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants to improve further and strengthen future work. Finally, the new directions of this research have been identified.

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