scholarly journals An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yike Li ◽  
Yingxiao Xiang ◽  
Endong Tong ◽  
Wenjia Niu ◽  
Bowei Jia ◽  
...  
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Image Feature ◽  
Connected Vehicle ◽  
Generative Adversarial Network ◽  
Security Issues ◽  
Adversarial Network ◽  
Control Units ◽  
Connected Vehicle Technology ◽  
Feature Based

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals Vehicle trajectory prediction and generation using LSTM models and GANs

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253868
Author(s):  
Luca Rossi ◽  
Andrea Ajmar ◽  
Marina Paolanti ◽  
Roberto Pierdicca
Keyword(s):  
Traffic Congestion ◽  
Short Term Memory ◽  
Autonomous Driving ◽  
Generative Models ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Displacement Error ◽  
Adversarial Network ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

scholarly journals Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Tian ◽  
Xiaorou Zhong ◽  
Ming Chen
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Generative Adversarial Network ◽  
Current Image ◽  
Adversarial Network ◽  
Complex Image ◽  
Segmentation Methods

Accurate remote sensing image segmentation can guide human activities well, but current image semantic segmentation methods cannot meet the high-precision semantic recognition requirements of complex images. In order to further improve the accuracy of remote sensing image semantic segmentation, this paper proposes a new image semantic segmentation method based on Generative Adversarial Network (GAN) and Fully Convolutional Neural Network (FCN). This method constructs a deep semantic segmentation network based on FCN, which can enhance the receptive field of the model. GAN is integrated into FCN semantic segmentation network to synthesize the global image feature information and then accurately segment the complex remote sensing image. Through experiments on a variety of datasets, it can be seen that the proposed method can meet the high-efficiency requirements of complex image semantic segmentation and has good semantic segmentation capabilities.

scholarly journals Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianfang Cao ◽  
Zibang Zhang ◽  
Aidi Zhao
Keyword(s):  
High Resolution ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Image Feature ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Set Up

Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image. Finally, the reconstructed high-resolution image is output to complete the design of the generative network. Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image. In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling. Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image. A mural dataset is set up by the current team. Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring. The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.

scholarly journals Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

2020 ◽  
Author(s):  
Zekun Chen ◽  
Linning Peng ◽  
Aiqun Hu ◽  
Hua Fu
Keyword(s):  
Prior Information ◽  
Learning Algorithm ◽  
Identification Accuracy ◽  
Generation Process ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
Device Identification ◽  
Security Issues ◽  
Adversarial Network ◽  
Rf Fingerprint

Abstract With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A USRP receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

scholarly journals A simulation system and speed guidance algorithms for intersection traffic control using connected vehicle technology

2019 ◽  
Vol 24 (2) ◽  
pp. 160-170 ◽  
Author(s):  
Shuai Liu ◽  
Weitong Zhang ◽  
Xiaojun Wu ◽  
Shuo Feng ◽  
Xin Pei ◽  
...  
Keyword(s):  
Traffic Control ◽  
Simulation System ◽  
Connected Vehicle ◽  
Connected Vehicle Technology ◽  
Vehicle Technology

scholarly journals A Generative and Entropy-Based Registration Approach for the Reassembly of Ancient Inscriptions

2021 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Roberto de de Lima-Hernandez ◽  
Maarten Vergauwen
Keyword(s):  
Poor Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Ancient Egyptian ◽  
Salient Points ◽  
Testing Data ◽  
Feature Based ◽  
Remote Sensing Techniques ◽  
Mesh Models

An increased interest in computer-aided heritage reconstruction has emerged in recent years due to the maturity of sophisticated computer vision techniques. Concretely, feature-based matching methods have been conducted to reassemble heritage assets, yielding plausible results for data that contains enough salient points for matching. However, they fail to register ancient artifacts that have been badly deteriorated over the years. In particular, for monochromatic incomplete data, such as 3D sunk relief eroded decorations, damaged drawings, and ancient inscriptions. The main issue lies in the lack of regions of interest and poor quality of the data, which prevent feature-based algorithms from estimating distinctive descriptors. This paper addresses the reassembly of damaged decorations by deploying a Generative Adversarial Network (GAN) to predict the continuing decoration traces of broken heritage fragments. By extending the texture information of broken counterpart fragments, it is demonstrated that registration methods are now able to find mutual characteristics that allow for accurate optimal rigid transformation estimation for fragments alignment. This work steps away from feature-based approaches, hence employing Mutual Information (MI) as a similarity metric to estimate an alignment transformation. Moreover, high-resolution geometry and imagery are combined to cope with the fragility and severe damage of heritage fragments. Therefore, the testing data is composed of a set of ancient Egyptian decorated broken fragments recorded through 3D remote sensing techniques. More specifically, structured light technology for mesh models creation, as well as orthophotos, upon which digital drawings are created. Even though this study is restricted to Egyptian artifacts, the workflow can be applied to reconstruct different types of decoration patterns in the cultural heritage domain.

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