scholarly journals Recent Advances of Image Steganography With Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 60575-60597
Author(s):  
Jia Liu ◽  
Yan Ke ◽  
Zhuo Zhang ◽  
Yu Lei ◽  
Jun Li ◽  
...  
Keyword(s):  
Image Steganography ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Recent Advances

scholarly journals Recent Advances of Generative Adversarial Networks in Computer Vision

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 14985-15006 ◽  
Author(s):  
Yang-Jie Cao ◽  
Li-Li Jia ◽  
Yong-Xia Chen ◽  
Nan Lin ◽  
Cong Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Recent Advances

scholarly journals A Novel Grayscale Image Steganography Scheme Based on Chaos Encryption and Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 168166-168176 ◽  
Author(s):  
Qi Li ◽  
Xingyuan Wang ◽  
Xiaoyu Wang ◽  
Bin Ma ◽  
Chunpeng Wang ◽  
...  
Keyword(s):  
Image Steganography ◽  
Generative Adversarial Networks ◽  
Grayscale Image ◽  
Chaos Encryption ◽  
Adversarial Networks

scholarly journals A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 38303-38314 ◽  
Author(s):  
Donghui Hu ◽  
Liang Wang ◽  
Wenjie Jiang ◽  
Shuli Zheng ◽  
Bin Li
Keyword(s):  
Image Steganography ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

Gastronomic Algorithms: Artistic and Sensory Exploration of Alain Passard's Michelin Plates in the Manner of Giuseppe Arcimboldo with GANs

Leonardo ◽  
2021 ◽  
pp. 1-11
Author(s):  
Emily L. Spratt
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Artistic Collaboration ◽  
Recent Advances ◽  
Learning Techniques ◽  
Data Scientist ◽  
The Senses ◽  
Computer Programmer

Abstract Although recent advances in artificial intelligence to generate images with deep learning techniques, especially generative adversarial networks (GANs), have offered radically new opportunities for its creative applications, there has been little investigation into its use as a tool to explore the senses beyond vision alone. In an artistic collaboration that brought Chef Alain Passard, art historian and data scientist Emily Spratt, and computer programmer Thomas Fan together, photographs of the three-star Michelin plates from the Parisian restaurant Arpège were used as a springboard to explore the art of culinary presentation in the manner of the Renaissance painter Giuseppe Arcimboldo.

scholarly journals Stegomalware: A Systematic Survey of Malware Hiding and Detection in Images, Machine Learning Models and Research Challenges

2021 ◽  
Author(s):  
Raj chaganti ◽  
vinayakumar R ◽  
Mamoun Alazab ◽  
Tuan Pham
Keyword(s):  
Machine Learning ◽  
Academic Research ◽  
Image Steganography ◽  
Machine Learning Techniques ◽  
Current Status ◽  
Generative Adversarial Networks ◽  
Malware Analysis ◽  
Source Of Infection ◽  
Adversarial Networks ◽  
File Formats

<div>Malware distribution to the victim network is commonly performed through file attachments in phishing email or downloading illegitimate files from the internet, when the victim interacts with the source of infection. To detect and prevent the malware distribution in the victim machine, the existing end device security applications may leverage sophisticated techniques such as signature-based or anomaly-based, machine learning techniques. The well-known file formats Portable Executable (PE) for Windows and Executable and Linkable Format (ELF) for Linux based operating system are used for malware analysis and the malware detection capabilities of these files has been well advanced for real time detection. But the malware payload hiding in multimedia like cover images using steganography detection has been a challenge for enterprises, as these are rarely seen and usually act as a stager in sophisticated attacks. In this article, to our knowledge, we are the first to try to address the knowledge gap between the current progress in image steganography and steganalysis academic research focusing on data hiding and the review of the stegomalware (malware payload hiding in images) targeting enterprises with cyberattacks current status. We present the stegomalware history, generation tools, file format specification description. Based on our findings, we perform the detail review of the image steganography techniques including the recent Generative Adversarial Networks (GAN) based models and the image steganalysis methods including the Deep Learning opportunities and challenges in stegomalware generation and detection are presented based on our findings.</div>

scholarly journals A Novel Approach Based on Modified Cycle Generative Adversarial Networks for Image Steganography

2020 ◽  
Vol 21 (1) ◽  
pp. 63-72
Author(s):  
P G Kuppusamy ◽  
K C Ramya ◽  
S Sheebha Rani ◽  
M Sivaram ◽  
Vigneswaran Dhasarathan
Keyword(s):  
Error Rate ◽  
Image Steganography ◽  
Generative Adversarial Networks ◽  
Hidden Information ◽  
Related Field ◽  
Discriminative Models ◽  
Digital World ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Training Stage

Image steganography aims at hiding information in a cover medium in an imperceptible way. While traditionalsteganography methods used invisible inks and microdots, digital world started using images and video files for hiding the secret content in it. Steganalysis is a closely related field for detecting hidden information in these multimedia files. There are many steganography algorithms implemented and tested but most of them fail during Steganalysis. To overcome this issue, in this paper, we are proposing to use generative adversarial networks for image steganography which include discriminative models to identify steganography image during training stage and that helps us to reduce the error rate later during Steganalysis. The proposed modified cycle Generative Adversarial Networks (Mod Cycle GAN) algorithm is tested using the USC-SIPI database and the experimentation results were better when compared with the algorithms in the literature. Because the discriminator block evaluates the image authenticity, we could modify the embedding algorithm until the discriminator could not identify the change made and thereby increasing the robustness.

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