Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning

2020 ◽  
Author(s):  
Arunkumar Sukumar ◽  
V. Subramaniyaswamy ◽  
Logesh Ravi ◽  
V. Vijayakumar ◽  
V. Indragandhi
Keyword(s):  
Deep Learning ◽  
Image Steganography ◽  
Histogram Shifting ◽  
Laplacian Pyramid ◽  
Robust Image

scholarly journals Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization

Plant Methods ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiong Xiong ◽  
Lingfeng Duan ◽  
Lingbo Liu ◽  
Haifu Tu ◽  
Peng Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Segmentation Method ◽  
Robust Image ◽  
Rice Panicles

RIWT and QR factorization based hybrid robust image steganography using block selection algorithm for IoT devices

2019 ◽  
Vol 36 (5) ◽  
pp. 4265-4276 ◽  
Author(s):  
S. Arunkumar ◽  
Subramaniyaswamy Vairavasundaram ◽  
K.S. Ravichandran ◽  
Logesh Ravi
Keyword(s):  
Image Steganography ◽  
Qr Factorization ◽  
Selection Algorithm ◽  
Robust Image ◽  
Iot Devices

On the fault-tolerant performance for a class of robust image steganography

2018 ◽  
Vol 146 ◽  
pp. 99-111 ◽  
Author(s):  
Yi Zhang ◽  
Chuan Qin ◽  
Weiming Zhang ◽  
Fenlin Liu ◽  
Xiangyang Luo
Keyword(s):  
Fault Tolerant ◽  
Image Steganography ◽  
Robust Image

scholarly journals Coverless Image Steganography Based on Generative Adversarial Network

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1394 ◽  
Author(s):  
Jiaohua Qin ◽  
Jing Wang ◽  
Yun Tan ◽  
Huajun Huang ◽  
Xuyu Xiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Poor Quality ◽  
Image Steganography ◽  
Cover Image ◽  
Secret Message ◽  
Generative Adversarial Network ◽  
Learning Framework ◽  
Adversarial Network ◽  
Traditional Image

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

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