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A Generative Adversarial Network Fused with Dual-Attention Mechanism and Its Application in Multitarget Image Fine Segmentation
2021
Computational Intelligence and Neuroscience
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, combining the image segmentation mechanism of generative adversarial network with the feature enhancement method of nonlocal attention, a generative adversarial network fused with attention mechanism ...
learn to focus on target structures of different shapes and sizes, highlight salient features useful for specific tasks, reduce the loss of image detail features, improve the accuracy of small-target ...
Acknowledgments is work was supported by the Shandong University of Science and Technology Research Fund, under Grant 2019TDJH102. ...
doi:10.1155/2021/2464648
pmid:34961814
pmcid:PMC8710171
fatcat:bbltst6hr5asdlrx4ztmlujx7a
Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
2020
Applied Sciences
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Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. ...
Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. ...
Conflicts of Interest: We declare that we have no conflict of interest. ...
doi:10.3390/app10051729
fatcat:biply6bjwzh65ntjmhawbokpoi
Game Theory for Adversarial Attacks and Defenses
[article]
2024
arXiv
pre-print
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2021 pre-print arXiv:2110.06166v1 fatcat:uwxeexgoybfmdngxidioj5tnpiOther Versions
To solve the game, each player would choose an optimal strategy against the opponent based on the prediction of the opponent's strategy choice. ...
Adversarial attacks can generate adversarial inputs by applying small but intentionally worst-case perturbations to samples from the dataset, which leads to even state-of-the-art deep neural networks outputting ...
FGSM [2] is an one-step attack algorithm, which only updates along the direction of gradient of adversary loss once. ...
arXiv:2110.06166v4
fatcat:tbf52frqkbhlrhcxg3hq6wqhbm
Image Rain Removal Using Conditional Generative Networks Incorporating
2022
Journal of Computer and Communications
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loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results. ...
We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ...
To improve the overall quality of these degraded images and ensure the performance of enhanced vision algorithms. ...
doi:10.4236/jcc.2022.102006
fatcat:z3k3gttlorcvheniatgomazigm
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
[article]
2024
arXiv
pre-print
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The objective of the experiment is to design and implement a fake face verification code and fraud detection system based on Generative Adversarial network (GANs) algorithm to enhance the security of the ...
This paper explores the application of Generative Adversarial Networks (GANs) in fraud detection, comparing their advantages with traditional methods. ...
This paper deeply discusses the integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms, providing me with rich theoretical support in experimental ...
arXiv:2402.09830v1
fatcat:bnk4exqbn5g4tmepg76kv4lb7i
A Survey on Super Resolution for video Enhancement Using GAN
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2312.16471v1 fatcat:uoho5hftjvdpvir5hv2uk67a24
This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial ...
The studies covered in these summaries provide fresh techniques to addressing the issues of improving image and video quality, such as recursive learning for video super-resolution, novel loss functions ...
In conclusion, the proposed enhanced superresolution reconstruction algorithm, based on a generative adversarial network, exhibits promising potential in achieving high-quality and aesthetically pleasing ...
arXiv:2312.16471v2
fatcat:vri7fii6tzbvbgeylzhdy7lmta
An attention directed generative adversarial network for retinal vessel segmentation
2022
Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)
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The attention directed generative adversarial networks consist of generator and discriminator. The generator uses U-Net architecture and combines the high-low feature attention modules. ...
The discriminator consists of a stack of residual modules, which together with the generator form a conditional generation adversarial network. ...
Therefore, we propose conditional generation adversarial network based on attention mechanism for enhancing fundus retinal segmentation. ...
doi:10.1117/12.2661840
fatcat:flb2ajz23ff65gs7o54zsxil4a
Generative Adversarial Network Based on Multi-feature Fusion Strategy for Motion Image Deblurring
2022
Diànnǎo xuékān
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In this paper, we propose a new generative adversarial network based on multi-feature fusion strategy for motion image deblurring. ...
information of the image, thus improving the ability of the network to adapt to image deformation. ...
Based on the above analysis, in order to improve the stability of adversarial network training and image quality reconstruction, this paper combines the perceptual loss [15] , pixel space loss [16] ...
doi:10.53106/199115992022023301004
fatcat:qprx2szja5dtfjlt67mljqxuc4
High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram
[article]
2019
arXiv
pre-print
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Inspired by image-to-image translation, we address this problem by using a learning-based post filter combining Pix2PixHD and ResUnet to reconstruct the mel-spectrograms together with super-resolution. ...
From the resulting super-resolution spectrogram networks, we can generate enhanced spectrograms to produce high quality synthesized speech. ...
translation model. 2) our proposed model can effectively improve speech quality based on generated enhanced spectrogram images. ...
arXiv:1912.01167v1
fatcat:bjcl5zcuofapxkt5f25h6gsr3q
Adversarial attack and defense in reinforcement learning-from AI security view
2019
Cybersecurity
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However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired ...
Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. ...
Acknowledgements The authors would like to thank the guidance of Professor Wenjia Niu and Professor Jiqiang Liu
Availability of data and materials Not applicable. ...
doi:10.1186/s42400-019-0027-x
fatcat:nlox7arfojaerietjz5ipskucm
Towards Enhancing Fault Tolerance in Neural Networks
[article]
2021
arXiv
pre-print
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The first game minimises the loss in reconstructing the input image for indistinguishability given the features from the Extractor, in the presence of a generative decoder. ...
The unsupervised training solves two games simultaneously in the presence of adversary neural networks with conflicting objectives to the Feature Extractor. ...
Instead of viewing the FT of Deep Learning systems from a hardware perspective, this work describes a framework based on game theory to improve the inherent FT of the NN application running on the hardware ...
arXiv:1907.03103v3
fatcat:rtywemwehjfxrm2civ6kri64qi
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1909.07558v3 fatcat:xqnazqvl5vd4lgfdbgkioidhjmOther Versions
The model also demonstrates a significant improvement on defense capability of adversarial examples. ...
The TTN is used to extend the texture samples of a clean image and helps classifiers focus on its shape. GAN is utilized to form a training framework for the model and generate the necessary images. ...
Acknowledgment This work was funded by the National Key R&D Program of China under grant 2017YFB1002502 and National Natural Science Foundation of China (No. 61701089 and No.61601518). ...
arXiv:1909.07558v5
fatcat:ru2p6xc5rva5di2tu7sfxygrru
Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels
2022
Computational Intelligence and Neuroscience
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This study develops an accurate method based on the generative adversarial network (GAN) that targets the issue of the current discontinuity of micro vessel segmentation in the retinal segmentation images ...
Experimental results demonstrate that the generative adversarial model, combined with deep convolutional neural network, enhances the segmentation accuracy of the retinal vessels far above that of certain ...
Although the generative adversarial networks are extensively used in the eld of image processing, they are rarely seen in retinal image processing [34] . erefore, this work focuses on the combination ...
doi:10.1155/2022/3585506
pmid:36072751
pmcid:PMC9441346
fatcat:cxhlkiz43zbath5jkifjlpdfrq
Attention-Guided Digital Adversarial Patches on Visual Detection
2021
Security and Communication Networks
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In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. ...
Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. ...
Red represents the effect of noise generated patches on image hiding. Whether the heatmap and adversarial patch based on YOLOv2 can be applied in other recognition networks also concerns us. ...
doi:10.1155/2021/6637936
fatcat:2hofkcwvarckdiee5ugqvmzazu
Introduction to the Special Section on Artificial Intelligence Security: Adversarial Attack and Defense
2021
IEEE Transactions on Network Science and Engineering
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Zhang et al. in "Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning" propose a secure virtual network embedding algorithm, which was essentially a secure network resource ...
Further, they defined an advanced adaptive attack based on intrusion-sharing incentive mechanism, and propose an IDS intelligent configuration scheme based on evolutionary game to detect our defined attack ...
doi:10.1109/tnse.2021.3073637
fatcat:ib5qh53qq5bu5hrfjejm3fp76i
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