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Analytical Survey of Image Steganography Algorithms in Spatial Domain


Call for Papers Engineering Journal, May 2019

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Analytical Survey of Image Steganography Algorithms in Spatial Domain

Yamini Joshi

Department of Computer Science and Engineering Jodhpur Institute of Engineering and Technology Jodhpur, India

yamini.1691@gmail.com

AbstractThis paper presents an overview of basic algorithms in Image Steganography in spatial domain. A novel technique employing Huffman encoding is also discussed. In this technique, spatial domain embedding techniques are chosen to embed the secret image which is initially Huffman encoded. The Huffman encoded image is then embedded on the pixels of cover image. As a result, a secret image which cannot be embedded in a normal LSB embedding technique can be embedded in this proposed technique since the secret image is compressed. Experimental results comparing aforementioned algorithms are also tabulated. Peak Signal to Noise Ratio (PSNR) value is the metric used in quantifying the distortion between images (stego and cover image).

KeywordsSteganography, PSNR, Huffman, Stego image, Cover image

  1. INTRODUCTION

    The word Steganography is derived from Greek words steganos , meaning "covered or protected," and graphei meaning "writing." It is a form of security through obscurity. It is a technique to hide secret information or message in some other data (generally known as cover or host) without any apparent evidence of data alteration.

    Steganography differs from cryptography. The latter is an art of secret writing, and is intended to make a message unreadable by a third party. It produces a string of 1s and 0s which can be perceived as gibberish but may give grabbers an impulse to decrypt it. Though grabbers are not able to decrypt the meaningless message due to lack of a secret key, they can simply destroy or delay the transformation process. Steganography has been proposed to fool grabbers from perceiving the existence of secret data and robustness is usually a common factor not taken into consideration.

    The capacity of secret data that can be embedded into a host image without degrading its quality is deemed much is more important than robustness. Thus, it does not hide the existence of the secret communication[1][2] .

    Although steganography is separate and distinct from cryptography, there are many analogies between the two, and

    some authors categorize steganography as a form of cryptography since hidden communication is a form of secret writing [2].

  2. STEGANOGRAPHY CONCEPTS

    1. Problem Formulation

      Although steganography is an ancient technique, its modern formulation is often given in terms of the prisoners problem proposed by Simmons[4], where two inmates Alice and Bob, wish to communicate in secret to chalk out an escape plan. All of their communication passes through a warden who will throw them in solitary confinement should she suspect any covert communication [5]. The warden, who is free to examine all communication exchanged between the inmates, can either be passive (examines and detects covert communication, reports it to some authority and lets the message through without blocking it) or active (alters the communication with the suspected hidden information deliberately, in order to destroy the information)[6].

    2. Nomenclature

      The file that is used to hide or embed secret data is known as a cover, host or envelop. The aim of steganography is to hide secret information inside the cover so that its presence cannot be detected. The words cover and host file will be used interchangeably henceforward to denote the envelope. The image produced after hiding secret information is known as the stego file.

    3. Kinds of steganography

    Almost all digital file formats can be used for steganography, but the formats that are more suitable are those with a high degree of redundancy. Redundancy can be defined as the bits of an object that provide accuracy far greater than necessary for the objects use and display [6]. Image and audio files especially comply with this criterion.

    Steganography

    Text Image Audio/Video Protocol Fig 1. Categorization of Steganography

    Figure 1 shows the four main categories of file formats that can be used for steganography. This categorization is based on the type of host or cover file used in the process.

    This paper will focus on Image Steganography where images are used to hide secret information. In the following sections, the secret information will also be an image file. Both the images are greyscale files. It not necessary that the secret image is embedded on the host image as is; some transformation can be done on the secret image bit stream but, in this case, the parameters of the transformation must be known by the receiving end to aid the extraction process. For simplicity, this paper has considered the secret image data as is.

  3. SPATIAL DOMAIN IMAGE STEGANOGRAPHY

    TECHNIQUES

    In this section, three basic techniques for Image Steganography are introduced: Simple LSB technique, LSB with substitution table technique, and modulus function technique. These techniques focus on pixels Least Significant Bit (LSB) modification of the host/cover image to hide secret data. In this subsection, the symbol k indicates the number of host bits that are used to embed secret data.

    1. Simple LSB Technique

      It is the simplest technique to embed secret information onto the cover image. Every pixel of the host contains some information of the secret image. For instance, if k LSBs of the host pixel are used to store secret information, then secret information is divided into groups of k and host pixels k LSBs are simply modified to reflect the secret image.

    2. LSB with Substitution Table Technique

      Although the LSB substitution works easily, it the quality of the host image is degraded quickly. In 2001, Wang et al. [7] first brought the concept of the substitution table. This substitution table provides a (transformed) value for each secret value so that the difference between k bits of LSBs of the host and respective k bits of the secret information to be embedded is minimal. After transforming the secret value to its corresponding value according to the substitution table, the transformed secret value is embedded to a host pixel. The representation of a substitution table is an N × N matrix STN x N={st[i][j] | 0<=i, j<=N-1 }, where the value N is equal to 2k. The substitution table is a binary matrix: every element of the matrix is either 0 or 1. There is one more constraint, Each row and column has one and only one 1, rest all values are 0. If ST[i][j] is 1, the 2-bit unit with value i will be transformed to value j. For instance, consider the substitution table below:

      ST4 x 4 =

      For the matrix above, k=2 and N=2k=22=4. Since ST[1][3]=1, two bit unit with value 1 or (01)2 will be transformed to 3 or (11)2.

      The main task here is to find a substitution table. As k increases, N increases and probable substitution matrices increase. Various binary matrices satisfy the constraint, but only some can make the transformed bits similar to the host bits, making host image degradation less. Finding a good substitution table for secret information and a host image is crucial. If a substitution table having transformed bits of the secret information most similar to the host image bits is found, then we can obtain the best quality stego-image. Wang et al. used a genetic algorithm to search a substitution table. However, only an approximately optimal substitution table can be found. In 2003, Chang et al. [8] proposed their ethod to find an optimal substitution table by applying the dynamic programming. This survey used the dynamic programming method proposed by Chang et al to implement the substitution table technique.

    3. Modulus Function Technique

    This technique was proposed by Thien and Lin [9]. Supposing k bits with decimal value x are to be embedded into the host images pixel with value y, this scheme proposed to find the value satisfying mod 2 k= x. Moreover, the value must be the closest value to y among all possible values that satisfy the equation mod 2k= x.

  4. PROPOSED METHOD

  1. Huffman Encoded Secret information

    In the techniques of Image Hiding discussed above, the size of image file that can be embedded onto the cover image file is restricted. For instance, consider a secret image file of dimensions h x w and cover of size H x W. the size of image file can be hidden in the cover is given by:

    h * w = k * H * W (1)

    where * denotes product or multiplication.

    The above equation specifies the maximum number of bits that can embedded and does not take into account any key that could also be embedded in order to facilitate the extraction of data.

    The proposed system employs Huffman encoding to compress secret images so that images which couldnt be hidden inside the cover due to size constraints can be embedded in the cover.

  2. Stego Image Architecture

    Since the outcomes of this method is not known, a secret image is embedded inside a host both with and without Huffman Compression to compare the effectiveness of this system. If the stego image containing Huffman Compressed secret image information depicts more distortion or high PSNR, the proposed method will not be considered effective.

    This system has also included a key in the stego image architecture to aid the extraction of secret image information. The key is very primitive and is always embedded in the last row of the host using Simple LSB technique with k = 2.

    0

    1

    2

    3

    m-1

    1

    LSB of every pixel = Secret Image data or

    Compressed Secret Image data

    P

    LSB of every pixel = Substitution table (if

    used)

    Q

    LSB of every pixel = Huffman decoder

    string(if used)

    R

    LSB of every pixel = Substitution table of

    Huffman decoder string

    .

    <unaltered host pixels>

    .

    .

    .

    n-1

    LSB of every pixel = Key parameters

    Fig. 2 Stego Image Architecture The stego key architecture is as follows:

    h

    w

    k

    a l

    S

    Tab

    _ro w

    h u f f

    huf f_l en

    huff_ row

    Huff_r_ sub

    Fig. 3 Key Structure

    where,

    h = Height of secret image w = Width of secret image

    k = no. of bits to be embedded in every pixels LSB al = algo used

    s = Size of secret image = h*w

    tab_row = Row no. of Substitution Table huff = Huffman Compression choice huff_row = Row of Huffman Decoder String

    huff_r_sub = Row of Substitution Table for Huffman Decoder string

  3. Experimental Results

    This section provides a comparison of methods discussed in Section III along with the proposed method based on the PSNR value of the stego image and embedding capacity of the cover. The images used as cover and secret information are 8 bit greyscale in .bmp format. The image Tulips with the size as shown in the Fig. 4 is used as the cover. Fig. 5 and Fig. 6. show two secret images Penguins and Colorboard which are used as secret information.

    Fig. 4. Tulips.bmp 700 x 800

    Fig. 5. Penguins.bmp 200 x 200

    Fig. 6. Colorboard.bmp 600 x 500

    55

    54

    53

    52

    51

    50

    49

    48

    47

    46

    45

    44

    43

    42

    41

    40

    39

    38

    37

    36

    35

    Simple LSB

    LSB with Substitutio n

    Modulus Function

    distortion in the cover image increases. It can also be concluded that if k is kept constant, the Modular function technique introduces the least amount of distortion (highest PSNR) in the cover image. Also, the Huffman compressed secret image data does not affect the PSNR values significantly. Thus, the proposed method of Huffman Compressing secret image information is an efficient way to hide images in the cover which couldnt be hidden otherwise due to their size constraints.

    The image Colorboard cannot be embedded in the cover, even with k= 4, due to its size constraints. But when Huffman Encoding is applied prior to embedding, the secret information compresses and is hidden in the cover.

    Table 1. MSE and PSNR values for stego image obtained by hiding Colorboard in the cover.

    HUFF

    Algo

    Colorboard.bmp(600×500)

    MSE

    PSNR

    0

    LSB

    0

    SUBS TABLE

    0

    MOD

    1

    LSB

    34.7723821429

    32.7184591706

    1

    SUBS TABLE

    31.7359571429

    33.1152875989

    1

    MOD

    17.2101392857

    35.7729597568

    2 3 4

    Fig. 7. PSNR values for secret image Penguins embedded

    in cover

    55

    54

    53

    52

    51

    50

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    48

    47

    46

    45

    44

    43

    42

    41

    40

    39

    38

    37

    36

    35

    2 3 4

    Simple LSB huff=1

    LSB with Substitution

    Modulus Function

    Table 2. MSE and PSNR values for stego image obtained by hiding Penguins in the cover for k=2

    HUFF

    Algo

    Penguins.bmp(200×200)

    MSE

    PSNR

    0

    LSB

    0.739553571429

    49.4411072218

    0

    SUBS TABLE

    0.698373214286

    49.6899278697

    0

    MOD

    0.427460714286

    51.8218415374

    1

    LSB

    0.725378571429

    49.5251563902

    1

    SUBS TABLE

    0.705208928571

    49.6476255865

    1

    MOD

    0.427371428571

    51.8227487633

    Fig. 8. PSNR values for secret image Penguins embedded in cover with Huffman Compression

    The graphs depicted above indicate that as k increases, the PSNR value of the stego image decreases and hence the

    Table 3. MSE and PSNR values for stego image obtained by hiding Penguins in the cover for k=3

    HUFF

    Algo

    Penguins.bmp(200×200)

    MSE

    PSNR

    0

    LSB

    2.048075

    45.0173450452

    0

    SUBS TABLE

    1.96221964286

    /td>

    45.2033274195

    0

    MOD

    1.05431071429

    47.9011174087

    1

    LSB

    2.01516964286

    45.0876874868

    1

    SUBS TABLE

    1.91883214286

    45.3004337603

    1

    MOD

    1.03564821429

    47.9786810019

    Table 4. MSE and PSNR values for stego image obtained by hiding Penguins in the cover for k=4

    HUFF

    Algo

    Penguins.bmp(200×200)

    MSE

    PSNR

    0

    LSB

    6.56844464286

    39.9561781673

    0

    SUBS TABLE

    5.2859625

    40.8995628306

    0

    MOD

    3.14430714286

    43.1555539859

    1

    LSB

    6.21054642857

    40.1995054805

    1

    SUBS TABLE

    5.82231428571

    40.4798471613

    1

    MOD

    3.0532875

    43.2831266113

    One potential problem of the approaches listed above is that there is no way of finding if the stego image is tampered

    with. If an intruder makes changes in the stego image, it will not be detected by the receiving end. In this aspect lies the major difference between Cryptography and Steganography, the former stresses on robustness while the later tries to hide the existence of secret information transmission.

    REFERENCES

    1. International Journal of Computer and Information Technology (ISSN: 2279 0764) Volume 01 Issue 02, November 2012, Steganography and Its Applications in Information Dessimilation on the Web Using Images as SecurityEmbeddment: A Wavelet Approach, pg 159

    2. Chi-Shiang CHAN and Chin-Chen CHANG, Department of Information Science and Applications, Asia University, Wufeng, Taiwan, A Survey of Information Hiding Schemes for Digital Images Chi-Shiang CHAN and Chin-Chen CHANG, IJCSES International Journal of Computer Sciences and Engineering Systems, Vol.1, No.3, July 2007

    3. Bauer, F. L. Decrypted Secrets: Methods and Maxims of Cryptology, 3rd ed. Springer-Verlag, New York, 2002

    4. Simmons, G., The prisoners problem and the subliminal channel,

      CRYPTO, 1983

    5. Chandramouli, R., Kharrazi, M. & Memon, N., Image steganography and steganalysis: Concepts and Practice, Proceedings of the 2nd International Workshop on Digital Watermarking, October 2003

    6. T. Morkel , J.H.P. Eloff , M.S. Olivier, AN OVERVIEW OF IMAGE STEGANOGRAPHY, Information and Computer Security Architecture (ICSA) Research Group, Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    7. C. C. Chang, J. Y. Hsiao, and C. S. Chan, "Finding Optimal Least- significant-bit Substitution in Image Hiding by Dynamic Programming Strategy," Pattern Recognition, vol. 36, no. 7, July 2003, pp. 1583-1595

    8. R. Z. Wang, C. F. Lin, and J. C. Lin, "Image Hiding by Optimal LSB Substitution and Genetic Algorithm," Pattern Recognition, vol. 34, no.3, March 2001, pp. 671-683.

    9. C. C. Thien, and J. C. Lin, "A Simple and High-hiding Capacity Method for Hiding Digit-by-digit Data in Images Based on Modulus Function," Pattern Recognition, vol. 36, no. 12, Dec. 2003, pp. 2875-2881

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