Robust image data hiding method based on multiple backups and pixel bit weight

2021 ◽  
pp. 1-14
Author(s):  
Yu Dong ◽  
Xianquan Zhang ◽  
Chunqiang Yu ◽  
Zhenjun Tang ◽  
Guoen Xia
Keyword(s):  
Data Hiding ◽  
Digital Images ◽  
Image Data ◽  
Experimental Results ◽  
Cover Image ◽  
Secret Image ◽  
Robust Image ◽  
Reference Matrix ◽  
Least Significant Bits

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image.

scholarly journals DIGDH: A Novel Framework of Difference Image Grafting Deep Hiding for Image Data Hiding

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 151
Author(s):  
Xintao Duan ◽  
Lei Li ◽  
Yao Su ◽  
Wenxin Wang ◽  
En Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Hiding ◽  
Deep Neural Networks ◽  
Random Distribution ◽  
Image Data ◽  
Experimental Results ◽  
The Real ◽  
Difference Image ◽  
Audio Media

Data hiding is the technique of embedding data into video or audio media. With the development of deep neural networks (DNN), the quality of images generated by novel data hiding methods based on DNN is getting better. However, there is still room for the similarity between the original images and the images generated by the DNN models which were trained based on the existing hiding frameworks to improve, and it is hard for the receiver to distinguish whether the container image is from the real sender. We propose a framework by introducing a key_img for using the over-fitting characteristic of DNN and combined with difference image grafting symmetrically, named difference image grafting deep hiding (DIGDH). The key_img can be used to identify whether the container image is from the real sender easily. The experimental results show that without changing the structures of networks, the models trained based on the proposed framework can generate images with higher similarity to original cover and secret images. According to the analysis results of the steganalysis tool named StegExpose, the container images generated by the hiding model trained based on the proposed framework is closer to the random distribution.

scholarly journals Ant Colony Optimization (ACO) based Data Hiding in Image Complex Region

2018 ◽  
Vol 8 (1) ◽  
pp. 379 ◽  
Author(s):  
Sahib Khan
Keyword(s):  
Ant Colony Optimization ◽  
Data Hiding ◽  
Experimental Results ◽  
Ant Colony ◽  
Cover Image ◽  
Complex Region ◽  
Secret Information ◽  
Lsb Substitution ◽  
Least Significant Bits

This paper presents data an Ant colony optimization (ACO) based data hiding technique. ACO is used to detect complex region of cover image and afterward, least significant bits (LSB) substitution is used to hide secret information in the detected complex regions’ pixels. ACO is an algorithm developed inspired by the inborn manners of ant species. The ant leaves pheromone on the ground for searching food and provisions. The proposed ACO-based data hiding in complex region establishes an array of pheromone, also called pheromone matrix, which represents the complex region in sequence at each pixel position of the cover image. The pheromone matrix is developed according to the movements of ants, determined by local differences of the image element’s intensity. The least significant bits of complex region pixels are substituted with message bits, to hide secret information. The experimental results, provided, show the significance of the performance of the proposed method.

High-Capacity Reversible Data Hiding Using Modified Pixel Value Ordering Approach

2018 ◽  
Vol 27 (11) ◽  
pp. 1850175 ◽  
Author(s):  
Neeraj Kumar Jain ◽  
Singara Singh Kasana
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Visual Quality ◽  
Experimental Results ◽  
Cover Image ◽  
Embedding Capacity ◽  
Signal Process ◽  
Pixel Value

The proposed reversible data hiding technique is the extension of Peng et al.’s technique [F. Peng, X. Li and B. Yang, Improved PVO-based reversible data hiding, Digit. Signal Process. 25 (2014) 255–265]. In this technique, a cover image is segmented into nonoverlapping blocks of equal size. Each block is sorted in ascending order and then differences are calculated on the basis of locations of its largest and second largest pixel values. Negative predicted differences are utilized to create empty spaces which further enhance the embedding capacity of the proposed technique. Also, the already sorted blocks are used to enhance the visual quality of marked images as pixels of these blocks are more correlated than the unsorted pixels of the block. Experimental results show the effectiveness of the proposed technique.

Robust data hiding with multiple backups and optimized reference matrix

2020 ◽  
Vol 39 (5) ◽  
pp. 6965-6977
Author(s):  
Xianquan Zhang ◽  
Ju Yang ◽  
Yu Dong ◽  
Chunqiang Yu ◽  
Zhenjun Tang
Keyword(s):  
Data Hiding ◽  
Data Extraction ◽  
Experimental Results ◽  
Cover Image ◽  
Secret Data ◽  
Stego Image ◽  
Group Data ◽  
Reference Matrix

Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks.

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

scholarly journals Data hiding using bit plane complexity segmentation steganography

2018 ◽  
Vol 7 (2.20) ◽  
pp. 33
Author(s):  
M Chandra Sekhar ◽  
S K. Chandini ◽  
V Sai Rohith ◽  
V Jhansi Lakshmi ◽  
M Pavan Kumar
Keyword(s):  
Data Hiding ◽  
Image Data ◽  
Limited Data ◽  
Information Measures ◽  
Bit Plane ◽  
Bit Planes ◽  
Human Attributes ◽  
Cover Up

Steganography is the basis of information covering the puzzle in some other data (we call it the ship), leaving no obvious evidence of data change. Most conventional steganographic strategies is limited data that hide the limit. They can cover up just 10% (or less) of the information measures of the vessel. This is on the grounds that the standard of those procedures was either to supplant an uncommon piece of the recurrence parts of the vessel picture, or to supplant all the slightest critical image bits with a secret multi-valued data. Our new Steganography uses the image as vessel information, and we enter the data in the bit-plane mystery vessel. This strategy makes use of human attributes, through which people cannot see the structure of any form of data exclusively damned pair, for example. We can replace most of the «Commotion like" regions in the bit-planes of the puzzle vessel image data without deteriorating the quality of the photos. We called our steganography "BPCS-steganography," which remains a Bit-Plane Complexity Segmentation steganography.  

Robust Image Data Hiding Using Geometric Mean Quantization

2009 ◽  
Author(s):  
Mohammad Ali Akhaee ◽  
Shahrokh Ghaemmaghami ◽  
Amir Nikooienejad ◽  
Farokh Marvasti
Keyword(s):  
Data Hiding ◽  
Geometric Mean ◽  
Image Data ◽  
Robust Image

scholarly journals Improving the Quality of Digital Images Using the Median Filter Technique to Reduce Noise

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 143 ◽  
Author(s):  
Annas Prasetio ◽  
Paska Marto Hasugian
Keyword(s):  
Digital Images ◽  
Color Image ◽  
Median Filter ◽  
Image Data ◽  
Original Image ◽  
Matrix Kernel ◽  
Filter Technique ◽  
Image Histogram ◽  
Point Line

The combination of point, line, shape and color elements combined to create a physical imitation of an object is called an image. The arrangement of the box elements in the image forms pixels or matrices. each image experiences degradation or loss of quality called noise. The effect of gaussian noise is the number of colored dots that are equal to the percentage of noise. This study raises the topic of improving the quality of digital images using median filter techniques to reduce noise. In this study using color image data (Red Green Blue) as test data and then converted into grayscale images to determine the gray degree of the image. The grayscale image is stored in the database. Then noise is generated by using random numbers. Noise in the form of impulse can be positive or negative in the form of adding pixel values to the original image, or it can reduce the value of the original image. The noise type used is salt & pepper. Gray degrees 0-255 spread. Can be calculated through image histograms. To reduce noise the median filter technique is used. Image histogram as a measure of the spread of numbers from the median filter. The result is a median filter can reduce noise salt and pepper by using a matrix kernel.

An Efficient Reversible Data Hiding Scheme for Encrypted Images

2018 ◽  
Vol 10 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Kai Chen ◽  
Dawen Xu
Keyword(s):  
Data Hiding ◽  
Efficient Method ◽  
Reversible Data Hiding ◽  
Additional Data ◽  
Emerging Technology ◽  
Experimental Results ◽  
Cover Image ◽  
Original Image ◽  
Secret Data ◽  
Encrypted Images

Reversible data hiding in the encrypted domain is an emerging technology, as it can preserve the confidentiality. In this article, an efficient method of reversible data hiding in encrypted images is proposed. The cover image is first partitioned into non-overlapping blocks. A specific modulo addition operation and block-scrambling operation are applied to obtain the encrypted image. The data-hider, who does not know the original image content, may reversibly embed secret data based on the homomorphic property of the cryptosystem. A scale factor is utilized for selecting embedding zone, which is scalable for different capacity requirements. At the receiving end, the additional data can be extracted if the receiver has the data-hiding key only. If the receiver has the encryption key only, he/she can recover the original image approximately. If the receiver has both the data-hiding key and the encryption key, he can extract the additional data and recover the original content without any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.

Sign in / Sign up

Export Citation Format

Share Document