scholarly journals Binary  Image  Steganography  based  on  Permutation

2021 ◽  
Author(s):  
Juvet Karnel Sadié ◽  
Stéphane Gael Raymond Ekodeck ◽  
Rene Ndoundam
Keyword(s):  
Binary Image ◽  
Image Steganography ◽  
Image Block ◽  
Hiding Capacity ◽  
Binary Images ◽  
Embedding Technique

Abstract We propose a steganographic scheme based on permutations, which improves the capacity of embedding information in a series of p host binary images. Given a host binary image block of size m x n bits and any embedding technique T, where T can hide Q(m, n) bits of data in the image; given p images, T can hide p x Q(m, n) bits of data in these images. Our scheme improves the capacity of embedding information in p images such that, instead of p x Q(m, n) bits, it can hide p x log2(p) + p x Q(m, n)bits. The results which have been obtained by experiments, show that our model performs a better hiding process in terms of hiding capacity.

Data Hiding in Document Images

2008 ◽  
pp. 291-304
Author(s):  
M. Chen ◽  
Nasir Memon ◽  
Edward K. Wong
Keyword(s):  
Digital Media ◽  
Digital Watermarking ◽  
Data Hiding ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Binary Image ◽  
Document Images ◽  
Hiding Capacity ◽  
Binary Images ◽  
Recent Developments

With the proliferation of digital media such as images, audio, and video, robust digital watermarking and data hiding techniques are needed for copyright protection, copy control, annotation, and authentication of document images. While many techniques have been proposed for digital color and grayscale images, not all of them can be directly applied to binary images in general and document images in particular. The difficulty lies in the fact that changing pixel values in a binary image could introduce irregularities that are very visually noticeable. Over the last few years, we have seen a growing but limited number of papers proposing new techniques and ideas for binary image watermarking and data hiding. In this chapter we present an overview and summary of recent developments on this important topic, and discuss important issues such as robustness and data hiding capacity of the different techniques.

Data Hiding in Document Images

2008 ◽  
pp. 438-450 ◽  
Author(s):  
Minya Chen ◽  
Nasir Memon ◽  
Edward K. Wong
Keyword(s):  
Digital Media ◽  
Digital Watermarking ◽  
Data Hiding ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Binary Image ◽  
Document Images ◽  
Hiding Capacity ◽  
Binary Images ◽  
Recent Developments

With the proliferation of digital media such as images, audio, and video, robust digital watermarking and data hiding techniques are needed for copyright protection, copy control, annotation, and authentication of document images. While many techniques have been proposed for digital color and grayscale images, not all of them can be directly applied to binary images in general and document images in particular. The difficulty lies in the fact that changing pixel values in a binary image could introduce irregularities that are very visually noticeable. Over the last few years, we have seen a growing but limited number of papers proposing new techniques and ideas for binary image watermarking and data hiding. In this chapter we present an overview and summary of recent developments on this important topic, and discuss important issues such as robustness and data hiding capacity of the different techniques.

Data Hiding in Document Images

2005 ◽  
pp. 231-247
Author(s):  
Minya Chen ◽  
Nasir Memon ◽  
Edward K. Wong
Keyword(s):  
Digital Media ◽  
Digital Watermarking ◽  
Data Hiding ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Binary Image ◽  
Document Images ◽  
Hiding Capacity ◽  
Binary Images ◽  
Recent Developments

With the proliferation of digital media such as images, audio, and video, robust digital watermarking and data hiding techniques are needed for copyright protection, copy control, annotation, and authentication of document images. While many techniques have been proposed for digital color and grayscale images, not all of them can be directly applied to binary images in general and document images in particular. The difficulty lies in the fact that changing pixel values in a binary image could introduce irregularities that are very visually noticeable. Over the last few years, we have seen a growing but limited number of papers proposing new techniques and ideas for binary image watermarking and data hiding. In this chapter we present an overview and summary of recent developments on this important topic, and discuss important issues such as robustness and data hiding capacity of the different techniques.

FAST IMPLEMENTATION OF MORPHOLOGICAL OPERATIONS USING BINARY IMAGE BLOCK DECOMPOSITION

2004 ◽  
Vol 04 (02) ◽  
pp. 183-202
Author(s):  
BASILIOS GATOS ◽  
STAVROS J. PERANTONIS ◽  
NIKOS PAPAMARKOS ◽  
IOANNIS ANDREADIS
Keyword(s):  
Binary Image ◽  
Morphological Operations ◽  
Image Block ◽  
Binary Images ◽  
Fast Implementation ◽  
Significant Acceleration ◽  
Different Types ◽  
Speed Up ◽  
The Look ◽  
Morphological Transformations

Morphological transformations are commonly used to perform a variety of image processing tasks. However, morphological operations are time-consuming procedures since they involve ordering and min/max computation of numbers resulting from image interaction with structuring elements. This paper presents a new method that can be used to speed up basic morphological operations for binary images. To achieve this, the binary images are first decomposed in a set of non-overlapping rectangular blocks of foreground pixels that have predefined maximum dimensions. Then off-line dilation and erosion of all rectangular blocks are arbitrary obtained and stored into suitable look-up array tables. By using the look up tables, the results of the morphological operations to the rectangular blocks are directly obtained. Thus, first all image blocks are replaced by their look-up array tables. Then the morphological operations are applied only to the limited number of the remaining pixels. Experimental results reveal that starting from a block represented binary image morphological operations can be executed with different types of structuring elements in significantly less CPU time. Using the block representation, we are able to perform dilation 16 times faster than non-fast implementations and 10 times faster than an alternative fast implementation based on contour processing. Significant acceleration is also recorded when using this approach for repeated application of dilation (for 10 iterations, dilation using the block representation is over 20 times faster than non-fast implementations and over four times faster than using the fast contour based approach).

scholarly journals Adaptive Matrix Pattern Steganography on RGB Images

2021 ◽  
Author(s):  
Amirfarhad Nilizadeh ◽  
Shirin Nilizadeh ◽  
Wojciech Mazurczyk ◽  
Cliff Zou ◽  
Gary T. Leavens
Keyword(s):  
Digital Media ◽  
Signal To Noise Ratio ◽  
Spatial Domain ◽  
Image Steganography ◽  
Image Block ◽  
Hiding Capacity ◽  
Ascii Character ◽  
Blue Channel ◽  
Rgb Images ◽  
Almost All

Almost all spatial domain image steganography methods rely on modifying the Least Significant Bits (LSB) of each pixel to minimize the visual distortions. However, these methods are susceptible to LSB blind attacks and quantitative steganalyses. This paper presents an adaptive spatial domain image steganography algorithm for hiding digital media based on matrix patterns, named “Adaptive Matrix Pattern” (AMP). The AMP method increases the security of the steganography scheme of largely hidden messages since it adaptively generates a unique codebook matrix pattern for each ASCII character in each image block. Therefore, each ASCII character gets a different codebook matrix pattern even in different regions of the same image. Moreover, it uses a preprocessing algorithm to identify the most suitable image blocks for hiding purposes. The resulting stego-images are robust against LSB blind attacks since the middle bits of green and blue channels generate matrix patterns and hiding secrets, respectively. Experimental results show that AMP is robust against quantitative steganalyses. Additionally, the quality of stego-images, based on the peak signal-to-noise ratio metric, remains high in both stego-RGB-image and in the stego-blue-channel. Finally, the AMP method provides a high hiding capacity, up to 1.33 bits per pixel.

scholarly journals A Novel Traffic Tracking System Based on division of Video into Frames and Processing

2013 ◽  
pp. 255-259
Author(s):  
Prabhakar Telagarapu ◽  
B. Jagdishwar Rao ◽  
J. Venkata Suman ◽  
K. Chiranjeevi
Keyword(s):  
Tracking System ◽  
Relative Size ◽  
Morphological Processing ◽  
Binary Image ◽  
Video Frame ◽  
Gray Scale ◽  
Binary Images ◽  
Morphological Image Processing ◽  
Car Detection ◽  
Analysis Technique

The objective of this paper is to visualize and analyze video.Videos are sequence of image frames. In this work, algorithm will be developed to analyze a frame and the same will be applied to all frames in a video. It is expected see unwanted objects in video frame, which can be removed by converting colour frames into a gray scale and implement thresh holding algorithm on an image. Threshold can be set depending on the object to be detected. Gray scale image will be converted to binary during thresh holding process. To reduce noise, to improve the robustness of the system, and to reduce the error rate in detection and tracking process, morphological image processing method for binary images is used. Morphological processing will be applied on binary image to remove small unwanted objects that are presented in a frame. A developed blob analysis technique for extracted binary image facilitates pedestrian and car detection. Processing blob’s information of relative size and location leads to distinguishing between pedestrian and car. The threshold, morphological and blobs process is applied to all frames in a video and finally original video with tagged cars will be displayed.

Secret Communication Techniques

2019 ◽  
pp. 1-19
Keyword(s):  
Digital Media ◽  
Quality Parameters ◽  
Image Steganography ◽  
Hiding Capacity ◽  
Video Steganography ◽  
Communication Techniques ◽  
Secret Communication ◽  
Text Steganography ◽  
Distortion Measure

This chapter introduces the reader to cryptography, steganography, watermarking, and quality parameters of image steganography techniques. Cryptography is a technique for secret communication. Steganography is a technique for secret and unnoticeable communication. The watermarking techniques hide watermarks inside the digital media. There are four types of steganography techniques: (1) image steganography, (2) audio steganography, (3) video steganography, and (4) text steganography. The quality of image steganographic algorithms can be measured by three parameters like (1) hiding capacity, (2) distortion measure, and (3) security check.

A Hybrid Lossless-Lossy Binary Image Compression Scheme

2013 ◽  
Vol 3 (4) ◽  
pp. 37-50
Author(s):  
Saif alZahir ◽  
Syed M. Naqvi
Keyword(s):  
Image Compression ◽  
Mathematical Models ◽  
Regression Models ◽  
Input Image ◽  
Binary Image ◽  
Large Set ◽  
Image Simulation ◽  
Compression Scheme ◽  
Binary Images ◽  
Visual Distortion

In this paper, the authors present a binary image compression scheme that can be used either for lossless or lossy compression requirements. This scheme contains five new contributions. The lossless component of the scheme partitions the input image into a number of non-overlapping rectangles using a new line-by-line method. The upper-left and the lower-right vertices of each rectangle are identified and the coordinates of which are efficiently encoded using three methods of representation and compression. The lossy component, on the other hand, provides higher compression through two techniques. 1) It reduces the number of rectangles from the input image using our mathematical regression models. These mathematical models guarantees image quality so that rectangular reduction should not produce visual distortion in the image. The mathematical models have been obtained through subjective tests and regression analysis on a large set of binary images. 2) Further compression gain is achieved through discarding isolated pixels and 1-pixel rectangles from the image. Simulation results show that the proposed schemes provide significant improvements over previously published work for both the lossy and the lossless components.

GRAPH-BASED THINNING FOR BINARY IMAGES

1993 ◽  
Vol 07 (05) ◽  
pp. 1009-1030 ◽  
Author(s):  
SATOSHI SUZUKI ◽  
NAONORI UEDA ◽  
JACK SKLANSKY
Keyword(s):  
Input Image ◽  
Binary Image ◽  
Feature Points ◽  
Shape Distortion ◽  
Binary Images ◽  
Ideal Line ◽  
Geographical Maps ◽  
Line Patterns

A thinning method for binary images is proposed which converts digital binary images into line patterns. The proposed method suppresses shape distortion as well as false feature points, thereby producing more natural line patterns than existing methods. In addition, this method guarantees that the produced line patterns are one pixel in width everywhere. In this method, an input binary image is transformed into a graph in which 1-pixels correspond to nodes and neighboring nodes are connected by edges. Next, nodes unnecessary for preserving the topology of the input image and the edges connecting them are deleted symmetrically. Then, edges that do not contribute to the preservation of the topology of the input image are deleted. The advantages of this graph-based thinning method are confirmed by applying it to ideal line patterns and geographical maps.

BINARY IMAGE WATERMARKING THROUGH BLURRING AND BIASED BINARIZATION

2005 ◽  
Vol 05 (01) ◽  
pp. 67-87 ◽  
Author(s):  
HAIPING LU ◽  
YUN Q. SHI ◽  
ALEX C. KOT ◽  
LIHUI CHEN
Keyword(s):  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
Image Watermarking ◽  
Binary Image ◽  
Experimental Results ◽  
Cosine Transform ◽  
Binary Images ◽  
Blurred Image ◽  
Binarization Threshold

Digital watermarking has been proposed for the protection of digital medias. This paper presents two watermarking algorithms for binary images. Both algorithms involve a blurring preprocessing and a biased binarization. After the blurring, the first algorithm embeds a watermark by modifying the DC components of the Discrete Cosine Transform (DCT), followed by a biased binarization, and the second one embeds a watermark by directly biasing the binarization threshold of the blurred image, controlled by a loop. Experimental results show the imperceptibility and robustness aspects of both algorithms.

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