Image steganography with N-puzzle encryption

Mukherjee, Srilekha; Sanyal, Goutam

doi:10.1007/s11042-020-09522-0

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Mukherjee and Sanyal [19] demonstrate the procedure of the N-puzzle encryption technique imposing the Arnold transformation on the cover image. The outcome of the process is a scrambling image, ensuring the pixel data and normal orientation.…”

Section: Related Workmentioning

confidence: 99%

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

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Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Most steganographic works for hiding data within images based on LSB has been performed to hide text data however recently different techniques of flattening the image matrix have become popular to store the image data within an image, however, when the payload file size is large, the cover media fails to accommodate the secret image [12][13]. Other techniques have evolved to accommodate data of a single image over multiple cover images but very lesser studies have been performed to store multiple secret images spread across multiple cover images [14][15]. This motivated us to develop this novel method to store multiple secret images across multiple cover images by flatmapping the image, splitting it into chunks and arranging JSON with metadata and graph based mapping.…”

Section: Introductionmentioning

confidence: 99%

Images Within Images? A Multi-image Paradigm with Novel Key-Value Graph Oriented Steganography

Adhikary¹

2022

Intelligent Computing &Amp; Optimization

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Steganographic methods have been in the limelight of research and development for concealing secret data within a cover media without being noticed through general visualization. The Least Significant Bits (LSBs) of 8bit color code for the RGB image arises the possibility of replacing the last two bits with the bits of the encrypted message. Several procedures have been developed to hide an image within another image however in most cases the payload image has to be within the accommodatable range of the cover image and very little literature have shown methods to hide multiple images within multiple images. This paper presents a novel approach to split the image into JSON styled dictionary of key-value pairs and using a metadata graph to locate different parts and positions of the payload images in the entire cluster of cover images. The model could be easily used in the real world scenario for privately sharing secret data over public communication channels without being noticed.

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Cover independent image steganography in spatial domain using higher order pixel bits

Maji¹,

Mandal

Sen

2021

Multimed Tools Appl

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Image steganography with N-puzzle encryption

Cited by 5 publications

References 23 publications

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Images Within Images? A Multi-image Paradigm with Novel Key-Value Graph Oriented Steganography

Cover independent image steganography in spatial domain using higher order pixel bits

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