scholarly journals A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload

2021 ◽  
Vol 7 (3) ◽  
pp. 24-31
Author(s):  
U. A. Md. Ehsan Ali ◽  
◽  
Emran Ali ◽  
Md. Sohrawordi ◽  
Md. Nahid Sultan
Keyword(s):  
Image Steganography ◽  
Selection For ◽  
High Payload

A novel technique for image steganography based on a high payload method and edge detection

2012 ◽  
Vol 39 (14) ◽  
pp. 11517-11524 ◽  
Author(s):  
Anastasia Ioannidou ◽  
Spyros T. Halkidis ◽  
George Stephanides
Keyword(s):  
Edge Detection ◽  
Image Steganography ◽  
Novel Technique ◽  
High Payload

High payload image steganography with reduced distortion using octonary pixel pairing scheme

2013 ◽  
Vol 73 (3) ◽  
pp. 2223-2245 ◽  
Author(s):  
C. Balasubramanian ◽  
S. Selvakumar ◽  
S. Geetha
Keyword(s):  
Image Steganography ◽  
High Payload

High-payload image steganography using two-way block matching

2006 ◽  
Vol 13 (3) ◽  
pp. 161-164 ◽  
Author(s):  
Ran-Zan Wang ◽  
Yeh-Shun Chen
Keyword(s):  
Block Matching ◽  
Image Steganography ◽  
High Payload

FFT-ETM based distortion less and high payload image steganography

2019 ◽  
Vol 78 (18) ◽  
pp. 25999-26022 ◽  
Author(s):  
Ahmed Khan ◽  
Aaliya Sarfaraz
Keyword(s):  
Image Steganography ◽  
High Payload

scholarly journals BASN—Learning Steganography with a Binary Attention Mechanism

2020 ◽  
Vol 12 (3) ◽  
pp. 43 ◽  
Author(s):  
Pin Wu ◽  
Xuting Chang ◽  
Yang Yang ◽  
Xiaoqiang Li
Keyword(s):  
Attention Mechanism ◽  
Image Steganography ◽  
Feature Maps ◽  
Security Issue ◽  
Image Steganalysis ◽  
Daily Lives ◽  
Research Attention ◽  
Secret Information ◽  
Payload Capacity ◽  
High Payload

Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.

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