A combination of least significant bit and deflate compression for image steganography

Steganography is one of the cryptography techniques where secret information can be hidden through multimedia files such as images and videos. Steganography can offer a way of exchanging secret and encrypted information in an untypical mechanism where communicating parties can only interpret the secret message. The literature has shown a great interest in the least significant bit (LSB) technique which aims at embedding the secret message bits into the most insignificant bits of the image pixels. Although LSB showed a stable performance of image steganography yet, many works should be done on the message part. This paper aims to propose a combination of LSB and Deflate compression algorithm for image steganography. The proposed Deflate algorithm utilized both LZ77 and Huffman coding. After compressing the message text, LSB has been applied to embed the text within the cover image. Using benchmark images, the proposed method demonstrated an outperformance over the state of the art. This can proof the efficacy of using Deflate as a data compression prior to the LSB embedding.

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Optimal Image Steganography Content Destruction Techniques

International Journal of Computers and Communications ◽

10.46300/91013.2021.15.14 ◽

2021 ◽

Vol 15 ◽

pp. 84-88

Author(s):

Siddeeq Y. Ameen ◽

Muthana R. Al-Badrany

Keyword(s):

Discrete Cosine Transform ◽

Image Steganography ◽

Cover Image ◽

Discrete Wavelet ◽

Least Significant Bit ◽

Hidden Information ◽

Random Data ◽

Cosine Transform ◽

Wavelet Techniques ◽

Lsb Technique

The paper presents two approaches for destroying steganogrphy content in an image. The first is the overwriting approach where a random data can be written again over steganographic images whereas the second approach is the denoising approach. With the second approach two kinds of destruction techniques have been adopted these are filtering and discrete wavelet techniques. These two approaches have been simulated and evaluated over two types of hiding techniques, Least Significant Bit LSB technique and Discrete Cosine Transform DCT technique. The results of the simulation show the capability of both approaches to destroy the hidden information without any alteration to the cover image except the denoising approach enhance the PSNR in any received image even without hidden information by an average of 4dB.

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Keamanan Pesan Rahasia Menggunakan Steganografi DCT (Discrete Cosine Transform) pada Citra JPEG

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v9i2.585 ◽

2019 ◽

Vol 9 (2) ◽

Author(s):

Dian Hafidh Zulfikar

Keyword(s):

Discrete Cosine Transform ◽

Spatial Domain ◽

Cover Image ◽

Secret Message ◽

Least Significant Bit ◽

Cosine Transform ◽

Secret Information ◽

Dct Coefficients ◽

Lsb Technique ◽

Dct Coefficient

Abstract The least significant-bit (LSB) based techniques are very popular for steganography in spatial domain. The simplest LSB technique simply replaces the LSB in the cover image with the bits from secret information. Further advanced techniques use some criteria to identify the pixels in which LSB(s) can be replaced with the bits of secret information. In Discrete Cosine Transform (DCT) based technique insertion of secret information in carrier depends on the DCT coefficients. Any DCT coefficient value above proper threshold is a potential place for insertion of secret information. Keywords : Discrete Cosine Transform (DCT), steganography, secret message AbstrakPada steganografi domain spasial, teknik least significant-bit (LSB) merupakan teknik yang paling banyak digunakan pada steganografi. Teknik yang sederhana yang hanya mengubah nilai LSB pada cover image dengan nilai bit pesan rahasia, atau dengan teknik yang lebih baik lagi yaitu dengan menentukan bit-bit LSB mana yang akan dilakukan pergantian nilai bit. Lain halnya dengan metode Discrete Cosine Transform (DCT), teknik steganografi ini akan menyembunyikan informasi rahasia tergantung dari nilai Koefisien DCT. Kata Kunci : Steganografi, DCT, Citra, JPEG, Pesan Rahasia

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Image Steganography combined with Cryptography

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v9i1.4160 ◽

2013 ◽

Vol 9 (1) ◽

pp. 976-984 ◽

Cited By ~ 1

Author(s):

Vijaya Lakshmi Paruchuri ◽

Dr.R. Sridevi ◽

K.S. SadaSiva Rao

Keyword(s):

Image Steganography ◽

Secret Message ◽

Secret Key ◽

Least Significant Bit ◽

Secret Data ◽

Encrypted Image ◽

Hidden Data ◽

Audio Files ◽

Lsb Technique ◽

The Right

Steganography is the science of invisible communication. Apart from the sender and intended recipient no one suspects the existence of the message. Using Steganography, information can be hidden in various mediums known as carriers. The carriers can be images, audio files, video files and text files. Image Steganography is a technique of using an image file as a carrier. Cryptography protects the information by applying the encryption and decryption techniques, so that the secret message can be understood only by the right person.This paper proposes a method, which combines the techniques of Steganography and cryptography, to hide the secret data in an image. In the first phase, the sender will embed the secret data in an image by using the Least Significant Bit (LSB) technique. The embedded image will be encrypted by using an encryption algorithm. At final, the encrypted image will be decrypted and the hidden data will be retrieved by supplying the valid secret key by the receiver. The process includes the phases of Data embedding, Image Encryption and recovery of both original image and secret data from the encrypted image.

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The use of Least Significant Bit (LSB) and Knight Tour Algorithm for image steganography of cover image

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp5218-5226 ◽

2019 ◽

Vol 9 (6) ◽

pp. 5218

Author(s):

Soo Ann Nie ◽

Ghazali Sulong ◽

Rozniza Ali ◽

Andrew Abel

Keyword(s):

Cover Image ◽

Secret Message ◽

Discrete Wavelet ◽

Receiver Side ◽

Test Image ◽

Least Significant Bit ◽

Stego Image ◽

Chi Square ◽

The Common ◽

Lsb Technique

Steganography is one of the method to communicate in a hidden way. In another word, steganography literally means the practice of hiding messages or information within another data. Previous studies have proposed various steganography techniques using different approaches including Least Significant Bit (LSB), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). However, different approaches still have its own weaknesses. Therefore image stenography using Knight Tour Algorithm with Least Significant Bit (LSB) technique is presented. The main objective is to improve the security factor in the stego image. Basically, the proposed technique is divided into two parts which are the sender and receiver side. Then, steganalysis which is a type of attack on stenography algorithm is used to detect the secret message in the cover image by the statistical analysis of pixel values. Chi Square Statistical Attach which is one of the type of steganalysis is used to detect these near-equal Po Vs in images and bases the probability of embedding on how close to equal the even pixel values and their corresponding odd pixel values are in the test image. The Knight Tour Algorithm is applied due to the common Least Significant Bit technique that is weak in security and easily decoded by outsider.

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Lossless text compression using GPT-2 language model and Huffman coding

SHS Web of Conferences ◽

10.1051/shsconf/202110204013 ◽

2021 ◽

Vol 102 ◽

pp. 04013

Author(s):

Md. Atiqur Rahman ◽

Mohamed Hamada

Keyword(s):

Data Compression ◽

State Of The Art ◽

Language Model ◽

Huffman Coding ◽

Original Text ◽

Text Compression ◽

Compression Technique ◽

Daily Life Activities ◽

Burrows Wheeler Transform ◽

Compressed Data

Modern daily life activities produced lots of information for the advancement of telecommunication. It is a challenging issue to store them on a digital device or transmit it over the Internet, leading to the necessity for data compression. Thus, research on data compression to solve the issue has become a topic of great interest to researchers. Moreover, the size of compressed data is generally smaller than its original. As a result, data compression saves storage and increases transmission speed. In this article, we propose a text compression technique using GPT-2 language model and Huffman coding. In this proposed method, Burrows-Wheeler transform and a list of keys are used to reduce the original text file’s length. Finally, we apply GPT-2 language mode and then Huffman coding for encoding. This proposed method is compared with the state-of-the-art techniques used for text compression. Finally, we show that the proposed method demonstrates a gain in compression ratio compared to the other state-of-the-art methods.

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Pixel Prediction-Based Image Steganography by Support Vector Neural Network

The Computer Journal ◽

10.1093/comjnl/bxaa017 ◽

2020 ◽

Author(s):

Reshma V K ◽

Vinod Kumar R S

Keyword(s):

Neural Network ◽

Medical Image ◽

Medical Information ◽

Similarity Index ◽

Image Steganography ◽

Secret Message ◽

Support Vector ◽

Discrete Wavelet ◽

Least Significant Bit ◽

Prediction Scheme

Abstract Securing the privacy of the medical information through the image steganography process has gained more research interest nowadays to protect the privacy of the patient. In the existing works, least significant bit (LSB) replacement strategy was most popularly used to hide the sensitive contents. Here, every pixel was replaced for achieving higher privacy, but it increased the complexity. This work introduces a novel pixel prediction scheme-based image steganography to overcome the complexity issues prevailing in the existing works. In the proposed pixel prediction scheme, the support vector neural network (SVNN) classifier is utilized for the construction of a prediction map, which identifies the suitable pixels for the embedding process. Then, in the embedding phase, wavelet coefficients are extracted from the medical image based on discrete wavelet transform (DWT) and embedding strength, and the secret message is embedded into the HL wavelet band. Finally, the secret message is extracted from the medical image on applying the DWT. The experimentation of the proposed pixel prediction scheme is done by utilizing the medical images from the BRATS database. The proposed pixel prediction scheme has achieved high performance with the values of 48.558 dB, 0.50009 and 0.9879 for the peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation factor, respectively.

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High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Security and Communication Networks ◽

10.1155/2021/4179340 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Xinliang Bi ◽

Xiaoyuan Yang ◽

Chao Wang ◽

Jia Liu

Keyword(s):

High Capacity ◽

Image Features ◽

Image Steganography ◽

Cover Image ◽

Secret Message ◽

The Internet ◽

Stego Image ◽

Style Transfer ◽

Secret Image ◽

Secret Information

Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

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A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography

International Journal of Digital Multimedia Broadcasting ◽

10.1155/2021/8827055 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Roseline Oluwaseun Ogundokun ◽

Oluwakemi Christiana Abikoye

Keyword(s):

Medical Information ◽

Medical Data ◽

Image Steganography ◽

Medical Information System ◽

Least Significant Bit ◽

Stego Image ◽

Performance Metric ◽

Challenging Tasks ◽

Multimedia Technologies ◽

Lsb Technique

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.

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Hiding Data in Color Image Using Least Significant Bits of Blue Sector

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/31.2.1948 ◽

2018 ◽

Vol 31 (2) ◽

pp. 193 ◽

Cited By ~ 2

Author(s):

Hussein L. Hussein

Keyword(s):

Color Image ◽

Cover Image ◽

Secret Message ◽

Mapping Technique ◽

Least Significant Bit ◽

Blue Sector ◽

Powerful Technique ◽

Secret Communication ◽

Least Significant Bits

Concealing the existence of secret hidden message inside a cover object is known as steganography, which is a powerful technique. We can provide a secret communication between sender and receiver using Steganography. In this paper, the main goal is for hiding secret message into the pixels using Least Significant Bit (LSB) of blue sector of the cover image. Therefore, the objective is by mapping technique presenting a model for hiding text in an image. In the model for proposing the secret message, convert text to binary also the covering (image) is divided into its three original colors, Red, Green and Blue (RGB) , use the Blue sector convert it to binary, hide two bits from the message in two bits of the least significant bits of blue sector of the image.

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Coverless Image Steganography Based on Generative Adversarial Network

Mathematics ◽

10.3390/math8091394 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1394 ◽

Cited By ~ 2

Author(s):

Jiaohua Qin ◽

Jing Wang ◽

Yun Tan ◽

Huajun Huang ◽

Xuyu Xiang ◽

...

Keyword(s):

Deep Learning ◽

Poor Quality ◽

Image Steganography ◽

Cover Image ◽

Secret Message ◽

Generative Adversarial Network ◽

Learning Framework ◽

Adversarial Network ◽

Traditional Image

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

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