scholarly journals A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 205766-205777
Author(s):  
Jianyi Liu ◽  
Yu Tian ◽  
Ru Zhang ◽  
Youqiang Sun ◽  
Chan Wang
Keyword(s):  
Semantic Content ◽  
Generative Adversarial Networks ◽  
Two Stage ◽  
Adversarial Networks ◽  
Example Generation ◽  
Adversarial Example

scholarly journals The Defense of Adversarial Example with Conditional Generative Adversarial Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fangchao Yu ◽  
Li Wang ◽  
Xianjin Fang ◽  
Youwen Zhang
Keyword(s):  
Defense Mechanism ◽  
Network Models ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Learning Tasks ◽  
Adversarial Examples ◽  
Adversarial Example ◽  
Network Approaches

Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear. Therefore, it is challenging to build a defense mechanism. In this paper, we propose an image-to-image translation model to defend against adversarial examples. The proposed model is based on a conditional generative adversarial network, which consists of a generator and a discriminator. The generator is used to eliminate adversarial perturbations in the input. The discriminator is used to distinguish generated data from original clean data to improve the training process. In other words, our approach can map the adversarial images to the clean images, which are then fed to the target deep learning model. The defense mechanism is independent of the target model, and the structure of the framework is universal. A series of experiments conducted on MNIST and CIFAR10 show that the proposed method can defend against multiple types of attacks while maintaining good performance.

Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks

2020 ◽  
Vol 17 (3) ◽  
pp. 401-405 ◽  
Author(s):  
Chenyang Zhang ◽  
Xuebing Yang ◽  
Yongqiang Tang ◽  
Wensheng Zhang
Keyword(s):  
Image Sequences ◽  
Radar Image ◽  
Generative Adversarial Networks ◽  
Two Stage ◽  
Adversarial Networks

scholarly journals Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

Sci ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 13 ◽  
Author(s):  
Marios Zachariou ◽  
Neofytos Dimitriou ◽  
Ognjen Arandjelović
Keyword(s):  
Empirical Evaluation ◽  
Semantic Content ◽  
Generative Adversarial Networks ◽  
Conceptual Problem ◽  
Artistic Style ◽  
Adversarial Networks ◽  
Large Numbers ◽  
Processing Step ◽  
Visual Reconstruction ◽  
Ancient Coin

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

scholarly journals Enhancing the Security of Deep Learning Steganography via Adversarial Examples

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1446
Author(s):  
Yueyun Shang ◽  
Shunzhi Jiang ◽  
Dengpan Ye ◽  
Jiaqing Huang
Keyword(s):  
Deep Learning ◽  
Automatic Generation ◽  
Generative Adversarial Networks ◽  
Learning Networks ◽  
Secret Image ◽  
Adversarial Networks ◽  
Linear Behavior ◽  
Adversarial Examples ◽  
Adversarial Example

Steganography is a collection of techniques for concealing the existence of information by embedding it within a cover. With the development of deep learning, some novel steganography methods have appeared based on the autoencoder or generative adversarial networks. While the deep learning based steganography methods have the advantages of automatic generation and capacity, the security of the algorithm needs to improve. In this paper, we take advantage of the linear behavior of deep learning networks in higher space and propose a novel steganography scheme which enhances the security by adversarial example. The system is trained with different training settings on two datasets. The experiment results show that the proposed scheme could escape from deep learning steganalyzer detection. Besides, the produced stego could extract secret image with less distortion.

scholarly journals Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 52
Author(s):  
Marios Zachariou ◽  
Neofytos Dimitriou ◽  
Ognjen Arandjelović
Keyword(s):  
Empirical Evaluation ◽  
Semantic Content ◽  
Generative Adversarial Networks ◽  
Conceptual Problem ◽  
Artistic Style ◽  
Adversarial Networks ◽  
Large Numbers ◽  
Processing Step ◽  
Visual Reconstruction ◽  
Ancient Coin

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

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