scholarly journals Steganografi QR Code pada Dual Carrier Image dengan Metode Least Significant Bit

2019 ◽  
Vol 5 (3) ◽  
pp. 261
Author(s):  
Yahya Risqi ◽  
Rudy Dwi Nyoto ◽  
Hafiz Muhardi
Keyword(s):  
Image Steganography ◽  
Qr Code ◽  
Least Significant Bit ◽  
Hiding Capacity ◽  
Blue Channel ◽  
Carrier Image ◽  
Salt And Pepper

Image Steganography adalah teknik untuk menyisipkan pesan rahasia ke dalam suatu citra digital, sehingga secara kasat mata manusia tidak akan mengetahui keberadaan dari pesan rahasia tersebut. Tujuan dari penetilian ini adalah menggunakan citra QR Code sebagai secret yang diubah ke dalam mode bitonal dengan 1 bit pada tiap pikselnya kemudian dipecah menjadi dua bagian dan disisipkan kedalam blue channel di dual carrier image sehingga kapasitas dari pesan yang akan disisipkan dapat meningkat. Penyisipan pada dual carrier image juga dapat meningkatkan keamanan karena pesan dapat dikirim secara terpisah. Penyisipan pesan dilakukan dengan metode substitusi Least Significant Bit (LSB). Untuk mengevaluasi model steganography yang diteliti, digunakan pengujian MSE and PSNR, Hiding Capacity (HC), Histogram, recovery dan noise. Hasil pengujian menunjukkan dengan menggunakan citra PNG dan TIFF pada HC hingga 95% nilai PSNR tetap tinggi yaitu sebesar 56 dB, dengan tingkat recovery 100% dan tahan terhadap jenis noise salt and pepper.

Improved Capacity Image Steganography Algorithm using 16-Pixel Differencing with n-bit LSB Substitution for RGB Images

2016 ◽  
Vol 6 (6) ◽  
pp. 2735 ◽  
Author(s):  
Meenakshi S Arya ◽  
Meenu Rani ◽  
Charndeep Singh Bedi
Keyword(s):  
Digital Media ◽  
Daily Basis ◽  
Image Steganography ◽  
The Internet ◽  
Least Significant Bit ◽  
Content Protection ◽  
Hiding Capacity ◽  
Pixel Value ◽  
Four Levels ◽  
Lsb Substitution

<p>With the intrusion of internet into the lives of every household and terabytes of data being transmitted over the internet on daily basis, the protection of content being transmitted over the internet has become an extremely serious concern. Various measures and methods are being researched and devised everyday to ensure content protection of digital media. To address this issue of content protection, this paper proposes an RGB image steganography based on sixteen-pixel differencing with n-bit Least Significant Bit (LSB) substitution. The proposed technique provides higher embedding capacity without sacrificing the imperceptibility of the host data. The image is divided into 4×4 non overlapping blocks and in each block the average difference value is calculated. Based on this value the block is classified to fall into one of four levels such as, lower, lower-middle, higher-middle and higher. If block belongs to lower level then 2-bit LSB substitution is used in it. Similarly, for lower-middle, higher-middle and higher level blocks 3, 4, and 5 bit LSB substitution is used. In our proposed method there is no need of pixel value readjustment for minimizing distortion. The experimental results show that stego-images are imperceptible and have huge hiding capacity.</p>

scholarly journals Improved Capacity Image Steganography Algorithm using 16-Pixel Differencing with n-bit LSB Substitution for RGB Images

2016 ◽  
Vol 6 (6) ◽  
pp. 2735
Author(s):  
Meenakshi S Arya ◽  
Meenu Rani ◽  
Charndeep Singh Bedi
Keyword(s):  
Digital Media ◽  
Daily Basis ◽  
Image Steganography ◽  
The Internet ◽  
Least Significant Bit ◽  
Content Protection ◽  
Hiding Capacity ◽  
Pixel Value ◽  
Four Levels ◽  
Lsb Substitution

<p>With the intrusion of internet into the lives of every household and terabytes of data being transmitted over the internet on daily basis, the protection of content being transmitted over the internet has become an extremely serious concern. Various measures and methods are being researched and devised everyday to ensure content protection of digital media. To address this issue of content protection, this paper proposes an RGB image steganography based on sixteen-pixel differencing with n-bit Least Significant Bit (LSB) substitution. The proposed technique provides higher embedding capacity without sacrificing the imperceptibility of the host data. The image is divided into 4×4 non overlapping blocks and in each block the average difference value is calculated. Based on this value the block is classified to fall into one of four levels such as, lower, lower-middle, higher-middle and higher. If block belongs to lower level then 2-bit LSB substitution is used in it. Similarly, for lower-middle, higher-middle and higher level blocks 3, 4, and 5 bit LSB substitution is used. In our proposed method there is no need of pixel value readjustment for minimizing distortion. The experimental results show that stego-images are imperceptible and have huge hiding capacity.</p>

scholarly journals Image Steganography using Improved Lsb-Mapping Technique with Enhanced Recovery Speed

2019 ◽  
Vol 8 (4) ◽  
pp. 11473-11478
Keyword(s):  
Data Hiding ◽  
Enhanced Recovery ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Cover Image ◽  
Mapping Technique ◽  
Secret Data ◽  
Logical Function ◽  
Hiding Capacity ◽  
Carrier Image

In recent days, for sending secret messages, we require secure internet. Image steganography is considered as the eminent tool for data hiding which provides better security for the data transmitted over internet. In the proposed work, the payload data is embedded using improved LSB-mapping technique. In this approach, two bits from each pixel of carrier image are considered for mapping and addition. Two bits of payload data can be embedded in one cover image pixel hence enhanced the hiding capacity. A logical function on addition is applied on 1st and 2nd bits of cover image pixel, and a mapping table is constructed which gives solution for data hiding and extraction. Simple addition function on stego pixel is performed to extract payload data hence increases the recovery speed. Here the secret data is not directly embedded but instead mapped and added with a number using modulo-4 strategy. Hence the payload data hidden using proposed approach provide more security and it can resist against regular LSB decoding approaches. The proposed work is implemented and tested for several gray scale as well as color images and compared with respect to parameters like peak signal to noise ratio and MSE. The proposed technique gives better results when compared and histogram of cover and stego images are also compared.

A Dynamic Approach to Image Steganography Using the Three Least Significant Bits and Extended Hill Cipher

2011 ◽  
Vol 403-408 ◽  
pp. 842-849 ◽  
Author(s):  
Gandharba Swain ◽  
Saroj Kumar Lenka
Keyword(s):  
Secure Communication ◽  
Block Cipher ◽  
Image Steganography ◽  
Secret Message ◽  
Least Significant Bit ◽  
Dynamic Approach ◽  
Cipher Text ◽  
Hill Cipher ◽  
Carrier Image ◽  
Least Significant Bits

In this paper we propose a technique for secure communication between sender and receiver. We use both cryptography and steganography. We take image as the carrier to use steganography. We have extended the existing hill cipher to increase its robustness and used it as our cryptography algorithm. By using this extended hill cipher (a new block cipher) which uses a 128 bit key, we encrypt the secret message. Then the cipher text of the secret message is embedded into the carrier image in 6th, 7th and 8th bit locations of some of the selected pixels (bytes). The 8th bit in a pixel (byte) is called as the least significant bit (LSB). The pixel selection is done depending on the bit pattern of the cipher text. So for different messages the embedding pixels will be different. That means to know the pixels of the image where the cipher text is embedded we should know the cipher text bits. Thus it becomes a stronger steganography. As the pixels where we embed are chosen during the run time of the algorithm, so we say that it is dynamic steganography. After embedding the resultant image will be sent to the receiver, the receiver will apply the reverse process what the sender has done and get the secret message.

scholarly journals High Capacity Image Steganography using Pixel Value Differencing Method with Data Compression using Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 1800-1804
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Data Hiding ◽  
Image Steganography ◽  
Data Embedding ◽  
Least Significant Bit ◽  
Secret Data ◽  
Stego Image ◽  
Compression Technique ◽  
Hiding Capacity

The Digital Market Is Rapidly Growing Day By Day. So, Data Hiding Is Going To Increase Its Importance. Information Can Be Hidden In Different Embedding Mediums, Known As Carriers By Using Steganography Techniques. The Carriers Are Different Multimedia Medium Such As Images, Audio Files, Video Files, And Text Files .There Are Several Techniques Present To Achieve Data Hiding Like Least Significant Bit Insertion Method And Transform Domain Technique. The Data Hidden Capacity Inside The Cover Image Totally Depends On The Properties Of The Image Like Number Of Noisy Pixels. Data Compression Provides To Hide Large Amount Of Secret Data To Increase The Capacity And The Image Steganography Based On Any Neural Network Provides That The Size And Quality Of The Stego-Image Remains Unaltered After Data Embedding. In This Paper We Propose A New Method Combined With Data Compression Along With Data Embedding Technique And After Embedding To Maintain The Quality The Communication Channel Use The Neural Network. The Compression Technique Increase The Data Hiding Capacity And The Use Of Neural Network Maintain The Flow Of Data Processing Signal

A Robust Image Steganography Technique Using Dynamic Embedding with Two Least Significant Bits

2011 ◽  
Vol 403-408 ◽  
pp. 835-841 ◽  
Author(s):  
Gandharba Swain ◽  
Saroj Kumar Lenka
Keyword(s):  
Secure Communication ◽  
Image Steganography ◽  
Evaluation Function ◽  
Least Significant Bit ◽  
Dynamic Evaluation ◽  
Secret Information ◽  
Cipher Text ◽  
Robust Image ◽  
Carrier Image ◽  
Selection Of

In this paper we are proposing a new Image steganography technique for secure communication between sender and receiver. At the sender we follow two steps. In the first step we encrypt the secret information by blowfish algorithm and in second step we embed the cipher text in LSB minus one and LSB (least significant bit) locations of some of the selected pixels (bytes) of the carrier image. One pixel is 8 bits in 8-bit gray scale. The selection of the pixels is done by a dynamic evaluation function. Depending on the cipher text bits, the dynamic evaluation function decides on which pixels the different cipher text bits are to be embedded. At the receiver also two steps are followed, first the cipher bits are retrieved from the image from the said locations and then it is decrypted by using the blowfish algorithm to get the secret information. As the embedding byte locations are decided based on bits of the cipher text, so it is dynamic steganography. This approach provides two levels of security, one at the cryptography level and the other at the steganography level. The proposed technique is experimented through a large number of experiments.

scholarly journals Adaptive Matrix Pattern Steganography on RGB Images

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

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

scholarly journals Image steganography using least significant bit and secret map techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 935
Author(s):  
Ashwaq Alabaichi ◽  
Maisa'a Abid Ali K. Al-Dabbas ◽  
Adnan Salih
Keyword(s):  
Quality Index ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Least Significant Bit ◽  
Secret Data ◽  
Hiding Capacity ◽  
Image Histogram ◽  
Image Fidelity ◽  
The Media ◽  
Key Sensitivity

In steganography, secret data are invisible in cover media, such as text, audio, video and image. Hence, attackers have no knowledge of the original message contained in the media or which algorithm is used to embed or extract such message. Image steganography is a branch of steganography in which secret data are hidden in host images. In this study, image steganography using least significant bit and secret map techniques is performed by applying 3D chaotic maps, namely, 3D Chebyshev and 3D logistic maps, to obtain high security. This technique is based on the concept of performing random insertion and selecting a pixel from a host image. The proposed algorithm is comprehensively evaluated on the basis of different criteria, such as correlation coefficient, information entropy, homogeneity, contrast, image, histogram, key sensitivity, hiding capacity, quality index, mean square error (MSE), peak signal-to-noise ratio (PSNR) and image fidelity. Results show that the proposed algorithm satisfies all the aforementioned criteria and is superior to other previous methods. Hence, it is efficient in hiding secret data and preserving the good visual quality of stego images. The proposed algorithm is resistant to different attacks, such as differential and statistical attacks, and yields good results in terms of key sensitivity, hiding capacity, quality index, MSE, PSNR and image fidelity.

scholarly journals Robust Secret Image Sharing Resistant to Noise in Shares

2021 ◽  
Vol 17 (1) ◽  
pp. 1-22
Author(s):  
Xuehu Yan ◽  
Lintao Liu ◽  
Longlong Li ◽  
Yuliang Lu
Keyword(s):  
Chinese Remainder Theorem ◽  
Recovery Phase ◽  
Error Correcting Codes ◽  
Secret Image Sharing ◽  
Data Loss ◽  
Least Significant Bit ◽  
Recovery Method ◽  
Secret Image ◽  
Image Sharing ◽  
Salt And Pepper

A secret image is split into   shares in the generation phase of secret image sharing (SIS) for a  threshold. In the recovery phase, the secret image is recovered when any   or more shares are collected, and each collected share is generally assumed to be lossless in conventional SIS during storage and transmission. However, noise will arise during real-world storage and transmission; thus, shares will experience data loss, which will also lead to data loss in the secret image being recovered. Secret image recovery in the case of lossy shares is an important issue that must be addressed in practice, which is the overall subject of this article. An SIS scheme that can recover the secret image from lossy shares is proposed in this article. First, robust SIS and its definition are introduced. Next, a robust SIS scheme for a  threshold without pixel expansion is proposed based on the Chinese remainder theorem (CRT) and error-correcting codes (ECC). By screening the random numbers, the share generation phase of the proposed robust SIS is designed to implement the error correction capability without increasing the share size. Particularly in the case of collecting noisy shares, our recovery method is to some degree robust to some noise types, such as least significant bit (LSB) noise, JPEG compression, and salt-and-pepper noise. A theoretical proof is presented, and experimental results are examined to evaluate the effectiveness of our proposed method.

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