The document summarizes a mathematical algorithm for quickly identifying steganographic signatures in images. It defines key concepts used in the algorithm such as the definition of an image, pixel neighborhood, pixel aberration, etc. The algorithm analyzes any given image and generates a "concentrating suspicion value" (Ξ) which is a numerical value indicating how likely the image contains hidden information embedded using concentrating steganographic algorithms. Images with higher Ξ values are more likely to contain stego information. The algorithm provides a fast way to filter images for more thorough interrogation.
Genetic Algorithm based Mosaic Image Steganography for Enhanced SecurityIDES Editor
Β
The concept of mosaic steganography was proposed
by Lai and Tsai [4] for information hiding and retrieval using
techniques such as histogram value, greedy search algorithm,
and random permutation techniques. In the present paper, a
novel method is attempted in mosaic image steganography
using techniques such as Genetic algorithm, Key based
random permutation .The creation of a predefined database
of target images has been avoided. Instead, the randomly
selected image is used as the target image reduces the enforced
memory load results reduction in the space complexity .GA is
used to generate a mapping sequence for tile image hiding.
This has resulted in better clarity in the retrieved secret image
as well as reduction in computational complexity. The quality
of original cover image remains preserved in spite of the
embedded data image, thereby better security and robustness
is assured. The mosaic image is yielded by dividing the secret
image into fragments and embed these tile fragments into
the target image based on the mapping sequence by GA and
permuted the sequence again by KBRP with a key .The recovery
of the secret image is by using the same key and the mapping
sequence. This is found to be a lossless data hiding method.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Β
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A comparatively study on visual cryptographyeSAT Journals
Β
Abstract The effective and secure protections of sensitive information are primary concerns in commercial, medical and military systems. To address the reliability problems for secret images, a visual cryptography scheme is a good alternative to remedy the vulnerabilities. Visual cryptography is a very secure and unique way to protect secrets. Visual cryptography is an encryption technique which is used to hide information which is present in an image. Unliketraditional cryptographic schemes, it uses human eyes to recover the secret without any complex decryption algorithms and the facilitate of computers. It is a secret sharing scheme which uses images distributed as shares such that, when the shares are superimposed, a hidden secret image is revealed.In this paper we represent various cryptography technique and research work done in this field. Keywords: Secret image sharing, cryptography, visual quality of image, pixel expansion
A NOVEL METHOD FOR THE CONSTRUCTION OF THRESHOLD MULTIPLE-SECRET VISUAL CRYPT...Editor IJCATR
Β
The main concept of the original visual secret sharing (VSS) scheme is to encrypt a secret image into n
meaningless share images. It cannot leak any information of the shared secret by any combination of the n share images
except for all of images. The shared secret image can be revealed by printing the share images on transparencies and
stacking the transparencies directly, so that the human visual system can recognize the shared secret image without using any
devices. The visual secrets sharing scheme for multiple secrets is called multiple-secret visual cryptographic schemes
(MVCSs). This paper proposed general constructions for threshold multiple-secret visual cryptographic schemes (MVCSs)
that are capable of encoding s secret images. This presented MVCS schemes utilize a predefined pattern book with pixel
expansion to encrypt secret images into share images. In our research, we propose a novel MVCS scheme that can share two
binary secret images on two rectangular share images with no pixel expansion, but also has an excellent recovery quality for
the secret images.
Implementation of Image Steganography Using 2-Level DWT Technique .............................................1
Aayushi Verma, Rajshree Nolkha, Aishwarya Singh and Garima Jaiswal
Efficient Neighbor Routing in Wireless Mesh Networks.......................................................................1
V. Lakshmi Praba and A. Mercy Rani
Content Based Messaging Model for Library Information System........................................................1
Surbhi Agarwal, Chandrika Chanda and Senthil Murugan B.
Building an Internal Cloud for IT Support Organisations: A Preview .....................................................1
S. M. M. M Kalyan Kumar and Dr S. C. Pradhan
Use of Intelligent Business, a Method for Complete Fulfillment of E-government ................................1
M. Nili Ahmadabadi, Masoud Najafi and Peyman Gholami
Comparison of Swarm Intelligence Techniques ...................................................................................1
Prof. S. A. Thakare
An Efficient Rough Set Approach in Querying Covering Based Relational Databases.............................1
P. Prabhavathy and Dr. B. K. Tripathy
Genetic Algorithm based Mosaic Image Steganography for Enhanced SecurityIDES Editor
Β
The concept of mosaic steganography was proposed
by Lai and Tsai [4] for information hiding and retrieval using
techniques such as histogram value, greedy search algorithm,
and random permutation techniques. In the present paper, a
novel method is attempted in mosaic image steganography
using techniques such as Genetic algorithm, Key based
random permutation .The creation of a predefined database
of target images has been avoided. Instead, the randomly
selected image is used as the target image reduces the enforced
memory load results reduction in the space complexity .GA is
used to generate a mapping sequence for tile image hiding.
This has resulted in better clarity in the retrieved secret image
as well as reduction in computational complexity. The quality
of original cover image remains preserved in spite of the
embedded data image, thereby better security and robustness
is assured. The mosaic image is yielded by dividing the secret
image into fragments and embed these tile fragments into
the target image based on the mapping sequence by GA and
permuted the sequence again by KBRP with a key .The recovery
of the secret image is by using the same key and the mapping
sequence. This is found to be a lossless data hiding method.
Image Encryption Based on Pixel Permutation and Text Based Pixel Substitutionijsrd.com
Β
Digital image Encryption techniques play a very important role to prevent image from unauthorized access. There are many types of methods available that can do Image Encryption, and the majority of them are scrambling algorithms based on pixel shuffling, which cannot change the histogram of an image. Hence, their security performances are not good. The encryption method that combines the pixel exchanging and gray level changing can handles reach a good chaotic effect. In this paper we focus on an image encryption technique based on pixel wise shuffling with the help of skew tent map and text based pixel substitution. The PSNR, NPCR and CC obtained by our technique shows that the proposed technique gives better result than the existing techniques.
A comparatively study on visual cryptographyeSAT Journals
Β
Abstract The effective and secure protections of sensitive information are primary concerns in commercial, medical and military systems. To address the reliability problems for secret images, a visual cryptography scheme is a good alternative to remedy the vulnerabilities. Visual cryptography is a very secure and unique way to protect secrets. Visual cryptography is an encryption technique which is used to hide information which is present in an image. Unliketraditional cryptographic schemes, it uses human eyes to recover the secret without any complex decryption algorithms and the facilitate of computers. It is a secret sharing scheme which uses images distributed as shares such that, when the shares are superimposed, a hidden secret image is revealed.In this paper we represent various cryptography technique and research work done in this field. Keywords: Secret image sharing, cryptography, visual quality of image, pixel expansion
A NOVEL METHOD FOR THE CONSTRUCTION OF THRESHOLD MULTIPLE-SECRET VISUAL CRYPT...Editor IJCATR
Β
The main concept of the original visual secret sharing (VSS) scheme is to encrypt a secret image into n
meaningless share images. It cannot leak any information of the shared secret by any combination of the n share images
except for all of images. The shared secret image can be revealed by printing the share images on transparencies and
stacking the transparencies directly, so that the human visual system can recognize the shared secret image without using any
devices. The visual secrets sharing scheme for multiple secrets is called multiple-secret visual cryptographic schemes
(MVCSs). This paper proposed general constructions for threshold multiple-secret visual cryptographic schemes (MVCSs)
that are capable of encoding s secret images. This presented MVCS schemes utilize a predefined pattern book with pixel
expansion to encrypt secret images into share images. In our research, we propose a novel MVCS scheme that can share two
binary secret images on two rectangular share images with no pixel expansion, but also has an excellent recovery quality for
the secret images.
Implementation of Image Steganography Using 2-Level DWT Technique .............................................1
Aayushi Verma, Rajshree Nolkha, Aishwarya Singh and Garima Jaiswal
Efficient Neighbor Routing in Wireless Mesh Networks.......................................................................1
V. Lakshmi Praba and A. Mercy Rani
Content Based Messaging Model for Library Information System........................................................1
Surbhi Agarwal, Chandrika Chanda and Senthil Murugan B.
Building an Internal Cloud for IT Support Organisations: A Preview .....................................................1
S. M. M. M Kalyan Kumar and Dr S. C. Pradhan
Use of Intelligent Business, a Method for Complete Fulfillment of E-government ................................1
M. Nili Ahmadabadi, Masoud Najafi and Peyman Gholami
Comparison of Swarm Intelligence Techniques ...................................................................................1
Prof. S. A. Thakare
An Efficient Rough Set Approach in Querying Covering Based Relational Databases.............................1
P. Prabhavathy and Dr. B. K. Tripathy
Image Steganography Using HBC and RDH TechniqueEditor IJCATR
Β
There are algorithms in existence for hiding data within an image. The proposed scheme treats the image as a whole. Here
Integer Cosine Transform (ICT) and Integer Wavelet Transform (IWT) is combined for converting signal to frequency. Hide Behind
Corner (HBC) algorithm is used to place a key at corners of the image. All the corner keys are encrypted by generating Pseudo
Random Numbers. The Secret keys are used for corner parts. Then the hidden image is transmitted. The receiver should be aware of
the keys that are used at the corners while encrypting the image. Reverse Data Hiding (RDH) is used to get the original image and it
proceeds once when all the corners are unlocked with proper secret keys. With these methods the performance of the stegnographic
technique is improved in terms of PSNR value.
TEXT STEGANOGRAPHY USING LSB INSERTION METHOD ALONG WITH CHAOS THEORYIJCSEA Journal
Β
The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
Β
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Protecting the data in a safe and secure way which does not impede the access of an authorized authority is an immensely difficult and very interesting research problem. image cryptography is a special type of encryption technique to obscure image-based secret information which can be decrypted by Human Visual System. Communication is the process of transmitting information from source to destination. The exchanging information should not be stolen by unauthorized parties like hackers while sending or receiving via channel. To avoid this stealing of the information visual cryptography techniques are used. This paper proposes a novel method for key generation by using nearest prime pixels. Further 2βs complement and logical operations are performed to generate decrypted image. The final decrypted image is generated by representing pixels in matrix form and data is retrieved in column wise.
IMAGE STEGANOGRAPHY USING BLOCK LEVEL ENTROPY THRESHOLDING TECHNIQUEJournal For Research
Β
Our modern civilization is based on Internet and sometimes it is required to keep the communication secret. It becomes possible by using two techniques: Cryptography and Steganography. The key concept behind both of two approaches is to hide information in anyway. There is little difference of these two approaches. Cryptography conceals the content of the secret message whereas Steganography is more advanced concept of the former. It embeds the secret message within a cover medium. Steganography is art and science in which the secret message is embedded into a cover medium so that no one else than the sender and the recipient can suspect it. So the third parties except the sender and receiver are imperceptible and unaware of the existence of the secret message. There are so many efficient Steganographic techniques like that text, image, audio, video and so on. This paper proposes only Image Steganographic method using Block Level Entropy Thresholding Technique.
AN ENHANCED SEPARABLE REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES USING SIDE M...Editor IJMTER
Β
This paper proposes a scheme for Enhanced Separable Reversible Data Hiding in
Encrypted images Using Side Match. In the first step the original image is encrypted using an
encryption key. Then additional data is embedded into the image by modifying a small portion of the
encrypted image using a data hiding key. With an encrypted image containing additional data, if a
receiver has the data hiding key, he can extract the additional data. If the receiver has the encryption
key, he can decrypt the image, but cannot extract the additional data. If the receiver has both the data
hiding key and encryption key, he can extract the additional data and recover the original content by
exploiting the spatial correlation in natural images. The accuracy of data extraction is improved by
using a better scheme for measuring the smoothness of the received image, and uses the Side Match
scheme to further decrease the error rate of extracted bits.
Analysis of image steganalysis techniques to defend against statistical attac...eSAT Publishing House
Β
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Steganography using Coefficient Replacement and Adaptive Scaling based on DTCWTCSCJournals
Β
Steganography is an authenticated technique for maintaining secrecy of embedded data. Steganography provides hardness of detecting the hidden data and has a potential capacity to hide the existence of confidential data. In this paper, we propose a novel steganography using coefficient replacement and adaptive scaling based on Dual Tree Complex Wavelet Transform (DTCWT) technique. The DTCWT and LWT 2 is applied on cover image and payload respectively to convert spatial domain into transform domain. The HH sub band coefficients of cover image are replaced by the LL sub band coefficients of payload to generate intermediate stego object and the adaptive scaling factor is used to scale down intermediate stego object coefficient values to generate final stego object. The adaptive scaling factor is determined based on entropy of cover image. The security and the capacity of the proposed method are high compared to the existing algorithms.
STEGANOGRAPHIC SUBSTITUTION OF THE LEAST SIGNIFICANT BIT DETERMINED THROUGH A...ijcsit
Β
ABSTRACT
The present workproposes to perform an analysis of the similarities between the least significant two bits of the cover image and multiple series of two-bit-length encrypted frames, all of them from the cryptomessage. After finding the most similar frame, we proceed to substitute it into the cover image; nevertheless, to provide a proof of the improvement from using itor the least similar one, the statistics from both cases are obtained.Providing information that the more similar the frame is, the better statistics the stego-image has. Moreover, the statistics obtained from our work are also compared with other works, finding that we provide a good scheme for hiding information.
Steganography is the technique of hiding the fact that communication is taking place,
by hiding data in other data. Many different carrier file formats can be used, but digital images
are the most popular because of their frequency on the Internet. For hiding secret information in
images, there exist a large variety of steganographic techniques. Steganalysis, the detection of this
hidden information, is an inherently difficult problem.In this paper,I am going to cover different
steganographic techniques researched by different researchers.
Keywords β Cryptography, Steganography, LSB, Hash-LSB, RSA Encryption βDecryption
Image Steganography Using HBC and RDH TechniqueEditor IJCATR
Β
There are algorithms in existence for hiding data within an image. The proposed scheme treats the image as a whole. Here
Integer Cosine Transform (ICT) and Integer Wavelet Transform (IWT) is combined for converting signal to frequency. Hide Behind
Corner (HBC) algorithm is used to place a key at corners of the image. All the corner keys are encrypted by generating Pseudo
Random Numbers. The Secret keys are used for corner parts. Then the hidden image is transmitted. The receiver should be aware of
the keys that are used at the corners while encrypting the image. Reverse Data Hiding (RDH) is used to get the original image and it
proceeds once when all the corners are unlocked with proper secret keys. With these methods the performance of the stegnographic
technique is improved in terms of PSNR value.
TEXT STEGANOGRAPHY USING LSB INSERTION METHOD ALONG WITH CHAOS THEORYIJCSEA Journal
Β
The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
Β
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Protecting the data in a safe and secure way which does not impede the access of an authorized authority is an immensely difficult and very interesting research problem. image cryptography is a special type of encryption technique to obscure image-based secret information which can be decrypted by Human Visual System. Communication is the process of transmitting information from source to destination. The exchanging information should not be stolen by unauthorized parties like hackers while sending or receiving via channel. To avoid this stealing of the information visual cryptography techniques are used. This paper proposes a novel method for key generation by using nearest prime pixels. Further 2βs complement and logical operations are performed to generate decrypted image. The final decrypted image is generated by representing pixels in matrix form and data is retrieved in column wise.
IMAGE STEGANOGRAPHY USING BLOCK LEVEL ENTROPY THRESHOLDING TECHNIQUEJournal For Research
Β
Our modern civilization is based on Internet and sometimes it is required to keep the communication secret. It becomes possible by using two techniques: Cryptography and Steganography. The key concept behind both of two approaches is to hide information in anyway. There is little difference of these two approaches. Cryptography conceals the content of the secret message whereas Steganography is more advanced concept of the former. It embeds the secret message within a cover medium. Steganography is art and science in which the secret message is embedded into a cover medium so that no one else than the sender and the recipient can suspect it. So the third parties except the sender and receiver are imperceptible and unaware of the existence of the secret message. There are so many efficient Steganographic techniques like that text, image, audio, video and so on. This paper proposes only Image Steganographic method using Block Level Entropy Thresholding Technique.
AN ENHANCED SEPARABLE REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES USING SIDE M...Editor IJMTER
Β
This paper proposes a scheme for Enhanced Separable Reversible Data Hiding in
Encrypted images Using Side Match. In the first step the original image is encrypted using an
encryption key. Then additional data is embedded into the image by modifying a small portion of the
encrypted image using a data hiding key. With an encrypted image containing additional data, if a
receiver has the data hiding key, he can extract the additional data. If the receiver has the encryption
key, he can decrypt the image, but cannot extract the additional data. If the receiver has both the data
hiding key and encryption key, he can extract the additional data and recover the original content by
exploiting the spatial correlation in natural images. The accuracy of data extraction is improved by
using a better scheme for measuring the smoothness of the received image, and uses the Side Match
scheme to further decrease the error rate of extracted bits.
Analysis of image steganalysis techniques to defend against statistical attac...eSAT Publishing House
Β
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Steganography using Coefficient Replacement and Adaptive Scaling based on DTCWTCSCJournals
Β
Steganography is an authenticated technique for maintaining secrecy of embedded data. Steganography provides hardness of detecting the hidden data and has a potential capacity to hide the existence of confidential data. In this paper, we propose a novel steganography using coefficient replacement and adaptive scaling based on Dual Tree Complex Wavelet Transform (DTCWT) technique. The DTCWT and LWT 2 is applied on cover image and payload respectively to convert spatial domain into transform domain. The HH sub band coefficients of cover image are replaced by the LL sub band coefficients of payload to generate intermediate stego object and the adaptive scaling factor is used to scale down intermediate stego object coefficient values to generate final stego object. The adaptive scaling factor is determined based on entropy of cover image. The security and the capacity of the proposed method are high compared to the existing algorithms.
STEGANOGRAPHIC SUBSTITUTION OF THE LEAST SIGNIFICANT BIT DETERMINED THROUGH A...ijcsit
Β
ABSTRACT
The present workproposes to perform an analysis of the similarities between the least significant two bits of the cover image and multiple series of two-bit-length encrypted frames, all of them from the cryptomessage. After finding the most similar frame, we proceed to substitute it into the cover image; nevertheless, to provide a proof of the improvement from using itor the least similar one, the statistics from both cases are obtained.Providing information that the more similar the frame is, the better statistics the stego-image has. Moreover, the statistics obtained from our work are also compared with other works, finding that we provide a good scheme for hiding information.
Steganography is the technique of hiding the fact that communication is taking place,
by hiding data in other data. Many different carrier file formats can be used, but digital images
are the most popular because of their frequency on the Internet. For hiding secret information in
images, there exist a large variety of steganographic techniques. Steganalysis, the detection of this
hidden information, is an inherently difficult problem.In this paper,I am going to cover different
steganographic techniques researched by different researchers.
Keywords β Cryptography, Steganography, LSB, Hash-LSB, RSA Encryption βDecryption
A NOVEL IMAGE STEGANOGRAPHY APPROACH USING MULTI-LAYERS DCT FEATURES BASED ON...ijma
Β
Steganography is the science of hidden data in the cover image without any updating of the cover image.
The recent research of the steganography is significantly used to hide large amount of information within
an image and/or audio files. This paper proposed a new novel approach for hiding the data of secret image
using Discrete Cosine Transform (DCT) features based on linear Support Vector Machine (SVM)
classifier. The DCT features are used to decrease the image redundant information. Moreover, DCT is
used to embed the secrete message based on the least significant bits of the RGB. Each bit in the cover
image is changed only to the extent that is not seen by the eyes of human. The SVM used as a classifier to
speed up the hiding process via the DCT features. The proposed method is implemented and the results
show significant improvements. In addition, the performance analysis is calculated based on the
parameters MSE, PSNR, NC, processing time, capacity, and robustness.
Enhancement of Payload Capacity for Image Steganography based on LSBEditor IJCATR
Β
In this result paper we will show the implementation result of our proposed method. Steganography is an art and
science of Hide the data in a cover image using some techniques that it remains undetected by the unauthorized access. We hide
the data in a manner that the stego image looks like a single entry by any third person. No one has doubt that the image is the
stego image. We use some different methods that keep data to be secret. It is a powerful tool for security with which we can
keep the data secret behind an object. An object may be Text, Audio, Video, and Image. The factor that affects the steganography
methods are PSNR, MSE, Payload Capacity and BER. Security of data will be shown by the Histogram of picture.
Steganography is going to gain its importance due to the exponential growth and secret communication of potential computer users over the internet [5]. It can also be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. Generally data embedding is achieved in communication, image, text, voice or multimedia content for copyright, military communication, authentication and many other purposes [2]. In image Steganography, secret communication is achieved to embed a message into cover image (used as the carrier to embed message into) and generate a stego- image (generated image which is carrying a hidden message)[1]. In this paper we have critically analyzed various steganographic techniques and also have covered steganography overview its major types, classification, applications [3]. KEYWORDS: STEGANOGRAPHY, STEGO IMAGE, COVER IMAGE, LSB
An image steganography using improved hyper-chaotic Henon map and fractal Tro...IJECEIAES
Β
Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixelβs image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.
A new image steganography algorithm basedIJNSA Journal
Β
In recent years, the rapid growth of information technology and digital communication has become very
important to secure information transmission between the sender and receiver. Therefore, steganography
introduces strongly to hide information and to communicate a secret data in an appropriate multimedia
carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has
been proposed to hide a large amount of secret data presented by secret color image. This algorithm is
based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the
DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach
has been applied before embedding process. The number of bit to be replaced at each byte is non uniform,
it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that
the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity
reached to four bits per byte.
RANDOMIZED STEGANOGRAPHY IN SKIN TONE IMAGESijcseit
Β
Steganography is the technique of hiding a confidential message in an ordinary message and the extraction
of that secret message at its destination. Different carrier file formats can be used in steganography.
Among these carrier file formats, digital images are the most popular. For this work, digital images are
used. Here steganography is done on the skin portion of an image. First skin portion of an image is
detected. Random pixels are selected from that detected region using a pseudo-random number generator.
The bits of the secret message will be embedded on the LSB of these random pixels. An analysis is done to
check the efficiency and robustness of the proposed method. The aim of this work is to show that
steganography done using random pixel selection is less prone to outside attacks.
RANDOMIZED STEGANOGRAPHY IN SKIN TONE IMAGES ijcseit
Β
Steganography is the technique of hiding a confidential message in an ordinary message and the extraction of that secret message at its destination. Different carrier file formats can be used in steganography. Among these carrier file formats, digital images are the most popular. For this work, digital images are used. Here steganography is done on the skin portion of an image. First skin portion of an image is detected. Random pixels are selected from that detected region using a pseudo-random number generator. The bits of the secret message will be embedded on the LSB of these random pixels. An analysis is done to check the efficiency and robustness of the proposed method. The aim of this work is to show that steganography done using random pixel selection is less prone to outside attacks.
Steganography is the technique of hiding a confidential message in an ordinary message and the extraction
of that secret message at its destination. Different carrier file formats can be used in steganography.
Among these carrier file formats, digital images are the most popular. For this work, digital images are
used. Here steganography is done on the skin portion of an image. First skin portion of an image is
detected. Random pixels are selected from that detected region using a pseudo-random number generator.
The bits of the secret message will be embedded on the LSB of these random pixels. An analysis is done to
check the efficiency and robustness of the proposed method. The aim of this work is to show that
steganography done using random pixel selection is less prone to outside attacks.
A NEW IMAGE STEGANOGRAPHY ALGORITHM BASED ON MLSB METHOD WITH RANDOM PIXELS S...IJNSA Journal
Β
In recent years, the rapid growth of information technology and digital communication has become very important to secure information transmission between the sender and receiver. Therefore, steganography introduces strongly to hide information and to communicate a secret data in an appropriate multimedia carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has been proposed to hide a large amount of secret data presented by secret color image. This algorithm is based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach has been applied before embedding process. The number of bit to be replaced at each byte is non uniform, it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity reached to four bits per byte.
Steganography is the technique of hiding the fact that communication is taking place,by hiding data in other data. Many different carrier file formats such as image, audio, video, DNA etc can be used, but digital images
are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques. In this paper different steganographic techniques are described.
International Journal of Computational Engineering Research(IJCER)ijceronline
Β
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Reference-free differential histogram-correlative detection of steganography:...nooriasukmaningtyas
Β
Recent research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless modified.
The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpret the concealed information inside digital medium such as image, audio and video. There are strong indications that hiding information inside digital medium has been used for planning criminal activities. In this way, it is important to develop a steganalysis technique which detects the existence of hidden messages inside digital medium. This paper focuses on universal image steganalysis method which uses RGB to HSI colour model conversion. Any Universal Steganalysis algorithm developed should be tested with various stegoimages to prove its efficiency. The developed Universal Steganalysis algorithm is tested in stego-image database which is obtained by implementing various RGB Least Significant Bit Steganographic algorithms. Though there are many stego-image sources available on the internet it lacks in the information such as how many rows has been infected by the steganography algorithms, how many bits have been modified and which channel has been affected. These parameters are important for Steganalysis algorithms and it helps to rate its efficiency. Proposed Steganalysis using Colour Model has been tested with our Image Database and the results were affirmative.
HYBRIDIZATION OF DCT BASED STEGANOGRAPHY AND RANDOM GRIDSIJNSA Journal
Β
With the increasing popularity of information technology in communication network, security has become an inseparable but vital issue for providing for confidentiality, data security, entity authentication and data origin authentication. Steganography is the scheme of hiding data into a cover media to provide confidentiality and secrecy without risking suspicion of an intruder. Visual cryptography is a new technique which provides information security using simple algorithm unlike the complex, computationally intensive algorithms used in other techniques like traditional cryptography. This technique allows visual information to be encrypted in such a way that their decryption can be performed by the Human Visual System (HVS), without any complex cryptographic algorithms. To provide a better secured system that ensures high data capacity and information security, a multilevel security system can be thought for which can be built by incorporating the principles of steganography and visual cryptography.
Cloud computing is a powerful, flexible, cost
efficient platform for providing consumer IT services
over the Internet. However Cloud Computing has
various level of risk because most important
information is maintained and managed by third party
vendors, which means harder to maintain security for
userβs data .Steganography is one of the ways to provide
security for secret data by inserting in an image or
video. In this most of the algorithms are based on the
Least Significant Bit (LSB), but the hackers easily
detects it embeds directly. An Efficient and secure
method of embedding secret message-extracting
message into or from color image using Artificial
Neural Network will be proposed. The proposed
method will be tested, implemented and analyzed for
various color images of different sizes and different
sizes of secret messages. The performance of the
algorithm will be analyzed by calculating various
parameters like PSNR, MSE and the results are good
compared to existing algorithms.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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In this second installment of our Essentials of Automations webinar series, weβll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
Weβll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether youβre tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Letβs turn complexity into clarity and make your workspaces work wonders!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. Whatβs changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4jβs graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more βmechanicalβ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Β
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Β
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Β
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager β Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Β
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Β
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Β
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Β
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
2. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
algorithms either embeds the information in the Least Significant Bits of the pixel or changes the entire color
code of the pixel by inserting information in more than 2 bits of the pixel. In former algorithms large number of
pixels are required for inserting information because only one or two LSB is available from every pixel and
hence known as Distributing Type while in the latter algorithm the entire information can be stored in very few
pixels because large numbers of bits are available from every pixel for storing information and hence called as
Concentrating Type.
Since the Suspicion Value related with the Distributing Steganographic algorithms (Termed as
Distributing Suspicion Value and represented by π²) is already calculated in [8]. So in this paper the suspicion
value related with the Concentrating Steganographic algorithms is being determined. This suspicion value
(related with concentrating stego algorithms) is here onwards termed as Concentrating Suspicion Value and
represented by πͺ in this entire paper. Based on this suspicion value (i.e. concentrating suspicion value πͺ)
calculated in this paper and the distributing suspicion value π² (determined in [8]) an overall suspicion value π»
for any given image is calculated. This overall suspicion value π» for any image is the holistic measure of the
presence of information hidden using any Spatial Domain Stego Algorithm (i.e. Concentrating as well as
Distributing algorithms) in the image and is termed as Spatial Domain Suspicion Value and represented here by
as π» in this entire paper.
II. PRELIMINARIES FOR DETERMINATION OF CONCENTRATING SUSPICION VALUE (πͺ)
The fast mathematical stego-identifier algorithm designed in this paper analyses any given digital
image (for the presence of Concentrating spatial domain steganographic signatures) and quickly generates a
Numerical Value (called in this text as Concentrating Suspicion Value and denoted by πͺ ) corresponding to
every image it has analyzed. This Suspicion Value is a number which is greater for those images which are more
likely to have stego information and lower for innocent images.
2.1 Preliminaries and Definition
Before we proceed to the technique of generating the Concentrating Suspicion Value πͺ for any image
we have to mathematically define the preliminary concepts to be used in this model. These preliminary concepts
are derived from the concepts mentioned in [6] and [7].
Definition 1 (Image)
Every digital image is collection of discrete picture elements or pixels. Let M be any digital image with
N pixels. So any particular pixel of image M is represented as M(z) and z can be any value from 1 to N. This
M(z) can be a gray level intensity of the pixel in gray scale image or RGB or YCbCr value of the pixel in a color
Image. The the individual RGB components of the pixel M(z) in image M is represented as MR(z), MG(z) and
MB(z) respectively. Thus M(z) can be a set { MR(z), MG(z) ,MB(z) } or equivalent gray scale representation or
(MR(z) + MG(z) + MB(z))/3. But it is always better to consider each R, G and B components individually
because the averaging effect cause loss of vital steganographic information. Further < {M},m > is multiset of
Image M such that M(z) β {M} for every z = 1 to N and m is a vector corresponding to the occurrence or count
of every element M(z) in {M}. Mathematically an image M with N pixels is explained in (1)
(1)
Definition 2 (Cardinality or Size of Image)
Any Image M consists of certain number of pixels. So any particular pixel of image M is represented as
M(z) and z can be any value from 1 to total number of pixels in the image. The cardinality or the size of the
image M is the total number of pixels present in the image and represented as n(M). So any Image M has n(M)
pixels.
(2)
Definition 3 (Component of an Image)
Any sub part of an Image is a component of the image. In other words any Image M can be broken
down into pixel groups (or clusters) and each such cluster forms a component of the image and is identified by
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3. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
its unique set of pixels. Thus in a Image M the Pixels M(z) from z = 1 to n(M) are the elements of the image M
and the subsets of the image M are composed of some of those pixels ( M(z) from z = 1 to n(M) ) and thus forms
the components of the image. Thus if the image M is broken in to K components then any component Mi of the
image M is mathematically explained as:
(3)
Also for every component Mi of the image M the Mi(z) represents the pixels of the component Mi and n(Mi)
represents the number of pixels in Mi.
Definition 4 (Neighborhood or Locality of Pixel)
If β(M(z)) is said to be set of neighboring pixels of any pixel M(z) in image M. Then any n i β β(M(z))
will be such that d(ni , M(z) ) β€ Ξ» where d is a function which calculates distance (can be Euclidean, City-Block,
Chess Board or any other type depending upon the steganographic algorithm) between its inputs (ie n i and
M(z)) and Ξ» is measurement of degree of neighbourhood and should be minimum (Generally equal to 1 pixel)
but also depends upon the steganographic algorithm used. Mathematically this can be represented as:
(4)
In Fig 1 an arbitrary pixel Y is shown with its immediate neighbors P, Q, R, S, T, U, V and W. We represent this
pixel Y as Y in mathematical notation. Thus β(Y) = {P, Q, R, S, T, U, V ,W} is set of neighboring pixels of pixel
Y. Here Ξ» = 1 and distance function d calculates Chess Board Distance.
Definition 5 (Adjacent Neighbors of Pixel)
Set of Adjacent Neighbors of a pixel M(z) is given as π (M(z)). Thus π (M(z)) is a collection of set
{M(x), M(y)} such that M(x) β β(M(z)) and M(y) β β(M(z)) and they are adjacent i.e d (M(x) , M(y)) = 1
where d is a function which calculates distance. Mathematically:
(5)
In Fig 1 for an arbitrary pixel Y with β(Y) = {P, Q, R, S, T, U, V ,W} the π(Y) = {{P,Q}, {Q,R)},
{R,T}, {T,W}, {W,V},{V,U},{U,S},{S,P}}.
Definition 6 (Pixel Aberration)
Pixel Aberration of any Pixel M(z) is the measure of the degree of difference of the given pixel M(z)
from its immediate neighborhood i.e. β(M(z)): d: βΊ Chess Board Distance β§ Ξ»=1 (immediate neighborhood is
obtained when Neighborhood or Locality Function β(M(z)) is calculated with Ξ» = 1 and distance function d is
used for determining Chess Board Distance) and represented as πΏ ( M(z) , β(M(z)). It is measured in terms of
Standard Deviation of β(M(z)) and acts like a quantifier which gives the idea of the amount of deviation of the
pixel from its immediate neighborhood.
Basic concept used for determining the pixel aberration of any pixel is based on the fact that, in any
natural image a pixel M(z) is expected to be as much different from its immediate neighborhood i.e. β(M(z)) as
the adjacent pairs of pixels in β(M(z)) themselves are. The same concept is explained in (6). Using simple
statistical techniques the concept developed in (6) is applied for determining the value of Pixel Aberration for
any Pixel in any given image. For any pixel M(z) in image M the mean of its absolute difference from its
immediate neighborhood β(M(z)) is given as (π π§ , β π π§ ). And the set representing the absolute differences
of the adjacent neighbors of M(z) among themselves is given as π(π (M(z))). The mean of the values of
π(π (M(z))) is given as π·(π (π(π§))) and Standard Deviation of the values of π(π (M(z))) is given as
π(π(π (M(z)))) . Since M(z) is also a immediate neighbor of every pixel in β(M(z)) so (π(π§), β(π(π§) )) must
be within the limits of standard deviation of π(π (M(z))) (represented as π(π(π (M(z)))) ) and mean of
π(π (M(z))) (represented as π·(π (π(π§))) ) . This degree of deviation of M(z) from its neighbors β(M(z)) in
www.iosrjournals.org 20 | Page
4. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
terms of π(π(π (M(z)))) and π·(π (π(π§))) is quantified as pixel aberration of pixel M(z) and represented as
πΏ ( M(z) , β(M(z))).
(6)
In terms of Fig 1 the mean of the differences of pixel Y with its neighbors i.e. elements of β(π) is given
as Y-P,Y-Q, Y-R, Y-S, Y-T, Y-U, Y-V and Y-W and should be close to the differences of the adjacent pixels in
β(Y) or in other words the difference of the pixel pairs in π(Y) i.e. difference of the elements of the pixel pairs
{P,Q}, {Q,R)}, {R,T}, {T,W}, {W,V}, {V,U}, {U,S} and {S,P} or simply P-Q, Q-R, R-T, T-W, W-V, V-U,
U-S and S-P. Thus ( Y , β Y ) is mean of modulus of Y-P, Y-Q, Y-R, Y-S, Y-T, Y-U, Y-V and Y-W and
π(π (Y)) = {modulus of P-Q, Q-R, R-T, T-W, W-V, V-U, U-S and S-P}. So aberration in pixel Y with respect
to its neighborhood β(Y) given as πΏ ( Y , β(Y)) should be within the limits of standard deviation of π(π(Y)) and
it mean π·(π(π Μ)) .
Mathematically:
(7)
Definition 7 (Pixel Aberration of the Entire Image (Weighted Mean))
In any image M with N pixels (i.e. n(M) = N) the Pixel aberration of image M is given as πΏ(π). It is a
quantifier whose high values for any given image M indicates that relatively large number of pixels in M have
high pixel aberration. It is calculated by determining the weighted mean of the modulus of the pixel aberrations
of the pixels of the entire image M.
Since for any image M the πΏ M z , β M z is the measure of deviation of M(z) from its
neighborhood β M z in terms of standard deviation so majority of pixels have this values located close to zero
and approximately more than 68% of the pixels have pixel aberration within 1 ( as per 3 Sigma or 68-95-99.7
rule of Statistics). Hence the simple mean of πΏ M z , β m z is very close to zero and is insignificantly
small for all images. Since by pixel aberration analysis we have to identify those images which have larger pixel
aberrations so as a remedy very small weights are assigned to less deviated values (majority of pixels which
have low pixel aberration values) and larger weights are assigned to more deviated values (few counted pixels
have large pixel aberrations). Thus value of πΏ(π) for the Image M with N pixels is given as:
(8)
Where the weight W(z) for the pixel M(z) is very small for majority of the pixels (which have
πΏ M z ,β m z value close to the mean value of the pixel aberration of all the pixels together in the image)
and quite large for the pixels having highly deviated values of πΏ M z , β m z (The value of
πΏ M z ,β m z for such pixels is very different from the mean of πΏ M z , β m z ) for all pixels
together). Such weights (which are larger for pixels having greater pixel aberration (in absolute terms) and much
smaller for pixels having lesser pixel aberration) can be computed by taking cube of the value of pixel
www.iosrjournals.org 21 | Page
5. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
aberration in terms of the standard deviation. In other words the weight W(z) for any Pixel M(z) in image M is
given as
(9)
2.2 Properties of Stego Image
Properties of stego-images (images containing information) depends upon the properties of those pixels
in the stego-image which are storing the information. In other words the properties of stego-images become
different from the innocent image (image without information) due to deformation produced in certain pixels of
stego-image due to embedding of information in those pixels. The basic concepts of steganalysis of Distributing
Stego Algorithms is given in Section 3.2 of [7] and the concepts related to steganalysis of Concentrating Stego-
Algorithms is given in Section 2.1 of [7] and Section 2.3.1 of [6] (Requirement 3 and Requirement 4).
From [6] and [7] it can be conclusively said that Information pixels (pixels containing hidden information) have
following 4 main properties:
1. Since the information pixels are suffering deformations so they are generally quite different from their
immediate neighbors. As a result the pixel aberration of information pixels is quite high. Since the
concentrating algorithms bring bigger changes in the pixel so Pixel Aberration based analysis is more
responsive to the steganalysis of Concentrating Stego-Algorithms.
2. Information has maximum concentration in the LSB Plane of the image. But the LSB Plane of any image
appears black and hence its contrast is increased by obtaining the Multicolored LSB Transform of the
image. Thus in the Multicolored LSB Transform we can clearly see the information pixels differently
colored from the innocent pixels. But since concentrating algorithms change only few pixels and as pixels
are very small in size so few counted modified pixels in the Multicolored LSB Plane are imperceptible to
human eye and are also statistical point of view are insignificantly less in number. But this method applies
perfectly well in steganalysis of Distributing Stego-Algorithms because they modify large number of pixels.
3. In any statistically significant component (50 x 50 pixels) of the Multicolored LSB Plane the distribution of
Red, Green and Blue components is significantly unequal among information pixels where as they are
nearly equal for innocent pixels. Thus the degree of deviation is more in the information pixels then the
innocent pixels.
4. The information pixels are always present in the Fine Grained Pixel Clusters and rarely in the Coarse
grained pixel clusters. They are always absent in the Continuous and Boulder Grained pixels Clusters. Refer
Section 3.2 of [7] for details of the classification of pixel clusters. The Multi Color LSB Transform of the
images with fine grained pixel clusters have majority of pixel with large value of Pixel Anomaly. Thus the
value of the Mean Pixel Anomaly is largest in the fine grained pixel clusters and is lesser in coarse grained
and even lesser in boulder grained and least in continuous grained pixel clusters.
2.3 Quantification of the Properties of Stego Image generated by Concentrating Stego Algorithm using
Pixel Aberration of the entire Image
These 4 properties associated with the stego-image, can be quantified in to an equivalent numerical
values corresponding to the given stego-image. The last three properties are associated with Distributing Stego
Algorithms and hence were used in determining Distributing Suspicion Value π² for any given image in [8]. By
using the definitions given in Section 2.1 the first property (related with Pixel Aberration and associated with
Concentrating Stego Algorithms) is used for determining the Concentrating Suspicion Value πͺ for any given
image. Both these numerical values (π² and πͺ) when combined together will be used for determining the holistic
Spatial Domain Suspicion Value π» associated with the image.
2.3.1 Quantification of the Properties Using Weighted Mean Pixel Aberration
Pixel Aberration based analysis responds well to all stego algorithms in general and Concentrating
Stego Algorithms in Particular . The concept of Pixel Aberration is based on [6] and [7] and is explained in
www.iosrjournals.org 22 | Page
6. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
detail in Definition 6 of Section 2.1 of this paper and mathematically represented in (6) and (7). Since the Pixel
Aberration is based on standard deviation so majority of pixels have pixel aberration close to 0. Thus in
Definition 7 of Section 2.1 of this paper the pixel aberration for the entire image is calculated by determining the
weighted mean of pixel aberrations of all the pixels in the given image and is mathematically represented in (8)
and (9). Thus we examine the performance of Weighted Mean Pixel Aberration as given in (8) and (9) as the
measure of Concentrating Suspicion Value πͺ for any given image. For this purpose we use two different 100 x
100 Pixel Images as the cover images. They are represented as A and B and shown in Fig 2. Three different
stego algorithms are used for embedding same information (this entire paragraph consisting of 1610 Characters)
in all the four images. The first two algorithms are of distributing type (named as Distributing Algorithm 1 and
Distributing Algorithm 2) and the third is of Concentrating type. Also the Distributing Algorithm 1 embeds the
secret information vertically (Column by Column) and Distributing Algorithm 2 embeds the secret message
horizontally (row by row). The three steganographic algorithms used in this paper namely Distributing
Algorithm 1, Distributing Algorithm 2 and Concentrating Algorithm were analyzed in [5] and are referred in
Section 5 of [5] as Algorithm designed in section 4, QuickStego Software and Eureka Steganographer
respectively. The stego-images obtained after inserting information from these three different algorithms are
represented as A1, A2, A3 and B1, B2, B3 respectively. Here only A3 and B3 are stego images corresponding to
Concentrating Algorithms and rest (A1, A2 and B1, B2) are obtained from Distributing Algorithms.
The Image B has the properties similar to most other images and hence is a perfect example of a
general image but Image A represents a special case of rare occurring images. The Image A is selected because
it is one such rare image which has pixel aberration in initial (Row by Row order) few pixels as almost zero. As
a result all other pixels (which do not have Pixel Aberration as absolutely zero) get very high weights causing
exceptionally high values of weighted mean pixel aberration for the entire image even though the mean (simple
mean) pixel aberration for the pixels of entire image is relatively very low. The same is shown in Table 1 and
Table 2. The values of Pixel Aberration for all these eight images (i.e. the Cover Images A and B and the
corresponding stego images A1, A2, A3 and B1, B2, B3 ) are graphically shown in Fig 3. By using (6),(7),(8)
and (9) the weighted mean pixel aberration and by using (11) the mean pixel aberration is calculated for all these
eight images and the same is tabulated in Table 1 and Table 2 respectively.
Weighted Mean Pixel Aberration for any image M is represented as πΉw(M) but its value is different for
all the three color components red, green and blue and hence the three color components are represented as
πΉwR(M), πΉwG(M) and πΉwB(M) respectively. The mean pixel aberration (mean of all the three color components)
and Maximum of the three color components represented as πΉwMEAN(M) and πΉwMAX(M) respectively is also
shown in Table 1. Also πΉwMAX(M) and πΉwMEAN(M)is explained mathematically in (10).
(10)
Also simple mean for any image M is represented by πΉm(M). Also πΉm(M) has 3 color components represented
as πΉmR(M), πΉmG(M) and πΉmB(M) and also the mean pixel aberration (mean of all the three color components)
and Maximum of the three color components represented as πΉmMEAN(M) and πΉmMAX(M) respectively.
The values of πΉmR(M), πΉmG(M) and πΉmB(M), πΉmMAX(M) and πΉmMEAN(M) for all these eight images is calculated
using (11) and tabulated in Table 2.
(11)
From Table 1 and Table 2 we can clearly see that even though the mean pixel aberration of image A is
lower than the Image B (Table 2) but still the weighted mean pixel aberration for rare occurring Image A is
many times higher (Table 1) than the regular image B (due to distortions in A as explained earlier). Thus we
clearly see that determination of the overall pixel aberration by using weighted mean pixel aberration introduces
certain unnecessary distortions in few images. Moreover the calculation of weighted pixel aberration requires
determination of standard deviation of the pixel aberration for all the pixels of the images and becomes highly
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7. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
complex and time consuming. In fact determination of weighted mean pixel aberration for large images (more
than 200 x 200 Pixels) consumes very high computational costs and hangs the program on most occasions. But
at the same time relying solely on the simple mean will also not yield a suitable result because for most large
images its value becomes insignificantly small. As a remedy a technique based on combination of simple means
and weighted means of the pixel aberration for entire image is used for determining the Concentrating Suspicion
Value πͺ for any given image.
2.3.2 Quantification of the Properties Using Combination of Simple and Weighted Mean Pixel
Aberration for Determination of Concentrating Suspicion Value
There are two different possible approaches of combining simple mean and weighted mean together for
calculation of the Concentrating Suspicion Value πͺ . Both these approaches are computationally fast because
they use a variant of Divide and Conquer Technique and hence break the entire image into small 5 x 5 to 10 x 10
Pixel Components.
In the first approach the individual simple mean Pixel Aberration for each component is calculated.
Using the values of the individual means the overall weighted mean for all the image components together can
be calculated as the value of overall Pixel Aberration for any given Image and represented by πΉ1(M) for any
Image M. The process of calculating πΉ 1(M) for any Image M is explained mathematically in (12). The
concentrating suspicion value obtained using Pixel Aberration of any given Image obtained by first approach i.e.
πΉ1(M) is represented by πͺ1(M) for any given image M.
In second approach the weighted mean pixel aberration is calculated for all the pixels in each component and
then an overall simple mean is calculated for weighted mean pixel aberration of each component. Pixel
Aberration for any given Image obtained by this approach is represented as πΉ2(M). The process of calculating
πΉ2(M) for any Image M is explained mathematically in (13). The concentrating suspicion value obtained using
Pixel Aberration of any given Image obtained by second approach i.e. πΉ2(M) is represented by πͺ2(M) for any
given image M.
2.3.2.1 Concentrating Suspicion Value Calculation by First Approach
The algorithm for calculating the Pixel Aberration for any given Image by combining the Simple and
Weighted Means together by First Approach is given in (12). On the basis of (12) the value of πΉ 1(M) is
calculated for the same images (Fig 2) and shown in Table 3. Since the calculation of πΉ1(M) uses Divide and
Conquer technique so its computation is much faster than the calculation of plain weighted mean pixel
aberration for entire image i.e. πΉw(M) as given in (8) and (9). But since the first approach of calculating overall
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8. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
pixel aberration i.e. πΉ1(M) uses weights so even this suffers from the same distortions as the weighted mean
pixel aberration πΉw(M) suffers. But the magnitude of πΉ1(M) multiplied by 10 is a pretty good indicator of
presence of information (stored using concentrating stego algorithms) in any image M and can be considered as
the measure of Concentrating Suspicion Value πͺ1(M) of image M.
But even more accurate method of determining Concentrating Suspicion Value can be based on the
combination of simple mean πΉmMEAN(M) and overall Pixel Aberration πΉ1(M). This combination can be done by
finding the product of πΉmMEAN(M) and πΉ1(M) and can be used as the second measure of concentrating suspicion
value πͺ2(M) for any image M. Thus the values πͺ1(M) and πͺ2(M) of Images in Fig 2 is calculated in Table 4 and
the algorithm for same is shown in (12). But the second measure of concentrating suspicion value πͺ2(M) suffers
from much higher complexity and hence consumes far higher computation time and computation power.
2.3.2.2 Concentrating Suspicion Value Calculation by Second Approach
The algorithm for calculating the Pixel Aberration for any given Image by combining the Simple and
Weighted Means together by Second Approach is given in (13). On the basis of (13) the value of πΉ2(M) is
calculated for the same images (Fig 2) and shown in Table 5. Since the calculation of πΉ2(M) also uses Divide
and Conquer Technique so like computation of πΉ1(M) even its computation is much faster than the calculation
of plain weighted mean pixel aberration for entire image i.e. πΉw(M) as given in (8) and (9). In Table 6 we have
determined the value of Concentrating Suspicion Value of any Image M represented as πͺ3(M) by using second
approach based on πΉ2(M) of any image M. The algorithm for calculating πͺ2(M) is given in (13).
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9. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
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10. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
2.3.2.3 Concentrating Suspicion Value πͺ(M) (Combining πͺ1(M), πͺ2(M) and πͺ3(M) in to single value)
In Section 2.3.2.1 and Section 2.3.2.2 the Concentrating Suspicion Value of any Image has been
calculated by three different methods using (12) and (13) respectively and represented as πͺ1(M), πͺ2(M) and
πͺ3(M) respectively. From (12) and (13) it can be clearly concluded that the Complexity of determining πͺ2(M) is
far higher than πͺ 1(M) or πͺ 3(M). Hence calculation of suspicion value using πͺ 2(M) is ruled out in all
circumstances. The performance of the two different types of concentrating suspicion values πͺ1(M) and πͺ3(M) is
examined by analyzing four different test images acting as cover images and corresponding Stego Images
obtained by two different stego softwares. These two softwares are used for embedding same information (1900
Characters) in all the four images as shown in Fig 4. These images (Fig 4) are of dimensions 600x800 , 275 x
181, 600 x 800 and 340 x 506 pixels and represented as A, B, C and D respectively. One of the two softwares
produces Stego Image by using Distributing Stego Algorithm while the other produces Stego image by using
Concentrating Stego Algorithm. The Stego Images produced by Distributing Stego Algorithms are represented
as A_d ,B_d ,C_d and D_d while the Stego Images produced by Concentrating Stego Algorithms are
represented as A_c, B_c, C_c and D_c.
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11. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
From Table 7 it can be easily concluded that πͺ3(M) is the true measure of Concentrating Suspicion
Value πͺ(M) of any Image M. The same can be mathematically written as:
(14)
2.4 Determination of Overall Suspicion Value π»
The values of Concentrating Suspicion Value πͺ(M) as obtained from Table 7 is combined with the
Distributing Suspicion Value π²(M) (determined from (17) in [8]) to Produce Overall Suspicion Values π»(M)for
the four different test images in Fig 4 (A,B,C and D)and the corresponding Stego Images(A_d ,B_d ,C_d and
D_d ;A_c, B_c, C_c and D_c). The same is shown in Table 8. Thus we see that Overall Suspicion Value π»(M)is
very much higher for all the images having hidden information while it is much lower for the innocent cover
images. The Overall Suspicon Value π»(M)for any image M is the maximum of the Concentrating and
Distributing Suspcion Values and mathematically given in (15).
(15)
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12. Quick Identification of Stego Signatures in Images using Suspicion Value (special reference to
III. CONCLUSION
The Distributing Suspicion Value (obtained from (17) in [8]) and Concentrating Suspicon Value
(obtained from (12) ,(13) and (14)) are combined together using (15) to produce Overall Suspicion Value π»(M)
associated with any given image M. From Table 8 it can be clearly seen that this numerical quantifier π»(M) is
higher for all those images which have some information embedded in them while it is much lower for all
innocent cover images. Hence this holistic suspicion value π»(M) (which applies on both the Concentrating as
well as Distributing Stego Algorithms) is a quick identifier of presence of information in any given image and
can be effectively used as Stego Identifier Algorithm. This fast stego-identification technique will find its
application in quick filtering of the suspicious images flowing through the web servers, routers, layer three
switches and all other electronic media concerned with transmission of images and will be very useful tool
against terrorists and all other mala-fide cyber networks.
IV. ACKNOWLEDGEMENT
I wish to dedicate this work to my parents Mr Chandan Kumar Choudhary and Mrs Nilima Choudhary
for providing the necessary support and encouragement in all walks of life including this work.
REFERENCES
[1]. Infosecurity Magazine article dated 02 May 2012 reports that Al-Qaeda uses Steganography to hide documents.
http://www.infosecurity-magazine.com/view/25524/alqaeda-uses-steganography-documents-hidden-in-porn-videos-found-on-
memory-stick
[2] Daily Mail Online, UK article dated 01 May 2012 reported that a Treasure trove of Intelligence was embedded in porn
video.http://www.dailymail.co.uk/news/article-2137848/Porn-video-reveals-Al-Qaeda-planns-hijack-cruise-ships-execute-
passengers.html#ixzz1uIgxpire
[3] The New York Times article dated Oct 30, 2001 with title βVeiled Messages of Terror May Lurk in Cyberspaceβ claims 9/11
attacks planned using Steganography.
[4] Wired article dated 02 nd July, 2001 nicknamed Bin Laden as βthe Steganography
Masterβhttp://www.wired.com/politics/law/news/2001/02/41658?currentPage=all
[5] Kaustubh Choudhary, Image Steganography and Global Terrorism, IOSR Volume 1, Issue 2, July 2012.
http://iosrjournals.org/journals/iosr-jce/papers/vol1-issue2/14/N0123448.pdf
[6] Kaustubh Choudhary , Mathematical Modeling of Image Steganographic System IOSR Volume 2, Issue 5, August 2012
http://iosrjournals.org/journals/iosr-jce/papers/vol2-issue5/A0250115.pdf
[7] Kaustubh Choudhary, Novel Approach to Image Steganalysis (A Step against Cyber Terrorism) IOSR Volume 2, Issue 5, August
2012 http://iosrjournals.org/journals/iosr-jce/papers/vol2-issue5/B0251628.pdf
[8] Kaustubh Choudhary, Identification of Steganographic Signatures of Distributing Stego Algorithms using Suspicion Value , IOSR
Volume 3, Issue 4, August 2012
[9] Kaustubh Choudhary, Identification of Stego Signatures in Images using Suspicion Value (special reference to Concentrating Stego
Algorithms), IOSR Volume 3, Issue 4, August 2012
ABOUT THE AUTHOR
Kaustubh Choudhary is Scientist in Defence Research and Development Organisation
(DRDO), Ministry of Defence, Government of India. He is currently attached with Indian
Navy at Indian Naval Ship, Shivaji as a faculty member of Naval College of Engineering.
He is young and dynamic scientist and has more than 5 Years of Experience in Teaching
and Research.
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