A COMPARATIVE STUDY OF STEGANOGRAPHIC DIGITAL WATERMARKING APPLICATIONS SUCH AS LSB, DCT, DFT AND DWT AND THEIR PERFORMANCE
EVALUATION USING METRICS LIKE MSE AND PSNR Saikat Bose1, Tripti Arjariya2
1Bhabha University, Computer Science Department, Bhopal, Madhya Pradesh, India
2Bhabha University, Computer Science Department, Bhopal, Madhya Pradesh, India
Abstract- For more than ten years, digital watermarking has been employed to protect the copyright information of digital media. Copyright information is inserted into the media files via a technique called digital watermarking. A text, image, audio, or video file can be the media file. The digital watermarking application uses steganography. Due to the internet's rapid expansion in potential computer information exchange, steganography has grown in significance. Steganography is different from cryptography in that it focuses on efficiently concealing the message in media as opposed to cryptography, which hides the contents of secret messages. An overview of steganography, its uses, and how it differs from cryptography is provided in this research article. The performance of the Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) is examined in this study article.
Keywords: Steganography, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT)
1 INTRODUCTION
By incorporating copyright information into the document, a technique called watermarking serves to safeguard it. In the media, the copyright information may be visible or invisible. In most cases, a watermark is invisible and does not alter the document's original text [1]. In the process of digital watermarking, copyright information is incorporated into the specific material in the form of a text, picture, audio, or video[8]. Digital media have been protected by watermarking from unauthorised parties' unauthorised access. To safeguard data from unauthorised organisations, numerous approaches like cryptography, watermarking, and steganography have been developed. Greek terms "steganos"
(covert) and "graphia" (writing) are the roots of the word "steganography" [15].
Steganography has been used to embed and retrieve copyright information using data encryption and decryption methods.
Both sender-side and receiver-side encryption and decryption algorithms are utilised [1]. To keep it hidden from the unauthorised world, copyright information is included in the material.
The copyright information is decoded at the receiving end to authenticate the originality of the media. The concept of
the image authentication process can be implemented in either of the two domains:
Spatial domain – Considered as a primitive and simple technique and is based on fabricating the secret data by directly modifying the pixel values of the host images. Some common algorithms include Least Significant Bit (LSB) Modification, Patchwork, Texture Block Coding, etc. But this technique generally lacks robustness and is more prone to image processing attacks.
Frequency domain– The ineffectiveness of the previous process can be rectified by using a transform domain approach. The information hiding mechanism in a transform space is gaining popularity because of the robustness against compression, other common filtering operations and noise. To embed a payload, a frequency transformation is applied to the host data and then modifications are made to the transform coefficients. Possible transformations include Z- transform, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) etc [21]. Steganography is used to covert communication. The secret image which is communicated to the destination is embedded into the cover image to derive the stego image [22].
Figure 1 Process of hiding data
Figure 2 Techniques of Steganography 2 REFERENCE CITATIONS
The Least Significant Bit (LSB) embedding approach, developed by Deshpande Neeta et al., conceals data using the cover image's least significant bits, which are invisible to the unaided eye [2]. The difficult task of transmitting embedded information to the target without being noticed was put forth by K. B. Raja and colleagues. The Least Significant Bit (LSB), Discrete Cosine Transform (DCT), and compression algorithms on raw images were used in this paper's image- based steganography to increase the payload's security [3]. To assure security against the steganalysis attack, Vijay Kumar Sharma et al. have developed a novel steganography algorithm for 8 bit grayscale or 24 bit color images based on logical operation [4]. The steganography tools algorithms were the focus of Chen Ming et al study's [6]. Various tools are categorized into five groups based on the analysis of the algorithms:
Category 1: Spatial domain based steganography tools.
Category 2: Transform domain based steganography tools.
Category 3: Document based steganography tools.
Category 4: File structure based Steganography tools.
Category 5: Other categories.
Aneesh Jain et al. presented a method that resists JPEG compression
and conceals data in bitmap images so that there is almost no discernible change between the original image and this new one [7]. A innovative method to conceal data in a colourful image using least significant bit was proposed by Beenish Mehboob, et al. in a discussion of the art and science of steganography in general [8]. A more reliable steganography method has been created that makes use of the advantages of the described techniques and stays clear of their limitations after Hassan Mathkour, et al., discussed criteria to study and evaluate the strengths and weaknesses of the techniques [9]. To conceal information in images, another method combines spread spectrum transmission, error control coding, and image processing [11].
Modern communication establishes hidden communication while using public channels, and is achieved by steganography, as explored by K. B. Shiva Kumar et al. The Coherent Steganography Technique Using Segmentation and Discrete Cosine Transform (CSSDCT) were put out in this research report. DCT was applied to each of the 8 x 8 blocks that made up the cover image. Based on the values of the DCT coefficients, the number of payload MSB bits was coercively inserted into the cover image's DCT coefficients. When compared to other research methodologies currently in use, it was shown that the proposed algorithm
had improved PSNR, Security, and capacity [12]. An optimum discrete wavelet transforms (DWT)-based steganography was proposed by T.
Narasimmalou et al. The peak signal to noise ratio (PSNR) produced by the suggested method was improved, according to experiments [14]. In addition to discussing how digital images might be used as a carrier to conceal messages, Arvind Kumar et al. also evaluate the effectiveness of several steganography programmes [16]. In order to evaluate the performance of the stego picture in terms of Peak Signal to Noise Ratio, Vijay Kumar et al. plan to examine the impact of embedding the secret message in various bands, such as CH, CV, and CD (PSNR).
Six different attacks have been used in experiments. According to experimental findings, using diagonal detail coefficients (CD) to replace error blocks leads in a higher PSNR than using alternative coefficients [17]. Juned Ahmed Mazumder et al have made a performance analysis of LSB, DFT and DWT by measuring MSE and PSNR changes with respect to different image formats and different message sizes.[21]
3 ALGORITHMS OF STEGANOGRAPHY 3.1 The following was how the LSB Based Steganography Encoding Algorithm [12] was Created:
Step 1: Read the written message that is buried in the cover image as well as the cover Image itself.
Step 2: Create a grayscale version of the colour image.
Step 3: Then binary conversion of text message.
Step 4: Determine the LSB for each pixel in the cover image.
Step 5: LSB of cover image is replaced with each bit of secret message one at a time.
Step 6: Stego image is derived.
3.2 The decoding algorithm [12] had been made in the manner described below.
Step 1: Check out the stego image.
Step 2: Determine the LSB for each pixel in the stego image.
Step 3: Obtain bits, and then transform each 8-bit value into a character.
3.3 The following was how the DCT Based Steganography encoding algorithm [12] was created:
Step 1: View the cover image.
Step 2: Convert the secret message to binary after reading it.
Step 3: The cover image is divided into 8 × 8 pixel blocks.
Step 4: In each pixel block, deduct 128 from top to bottom while moving from left to right.
Step 5: Each block is subjected to the DCT.
Step 6: Through a quantization table, each block is compressed.
Step 7: Replace each bit of the secret message with the LSB calculated for each DC coefficient.
Step 8: Stego image is derived.
3.4 The decoding algorithm [12] had been made in the manner described below:
Step 1: The stego image is taken.
Step 2: Stego picture is divided into 8 × 8 pixel blocks.
Step 3: In each pixel block, deduct 128 from top to bottom while moving from left to right.
Step 4: Each block is subjected to the DCT.
Step 5: Through a quantization table, each block is compressed.
Step 6: Determine the LSB for every DC coefficient.
3.5 The following was how the DFT Based Steganography encoding algorithm [19], [20] was created:
Step 1: The secret image is taken.
Step 2: First we calculate the length of the secret image.
Step 3: Convert the image into ASCII format.
Step 4: Convert the cover image from its spatial domain into its spatial domain using DFT.
Step 5: Since we know that Fourier transform are complex, secret information is embedded only in the real parts.
Step 6: After insertion of the secret information, Inverse DFT is taken to get stego image.
3.6 The decoding algorithm [19],[20]
had been made in the manner described below:
Step 1: Read stego image.
Step 2: Inverse DFT is applied to the stego image.
Step 3: Find the length of the image.
Step 4: Then extract the real co-efficient from the DFT representation up to the length of the message.
3.7 The following was how the DWT Based Steganography encoding algorithm [5] was created:
Step 1: Read the text message that is concealed in the cover image as well as the cover image itself.
Step 2: The text message should be binary-ized. On the cover image, apply the 2D Haar transform.
Step 3: Obtain the cover image's horizontal and vertical filtering coefficients.
Cover image is added with data bits for DWT coefficients.
Step 4: Stego image is derived.
Step 5: Determine the stego image's mean square error (MSE) and peak signal to noise ratio (PSNR).
3.8 The decoding algorithm [5] had been made in the manner described below:
Step 1: Read stego image.
Step 2: Determine the cover image's horizontal and vertical filtering coefficients.
Step 3: Reassemble the cover image after piecemeal message extraction.
Step 4: Stego image is derived.
Step 5: Create a message vector from the data. Check it against the original message.
4 STEGANOGRAPHIC TECHNIQUES A stego system encoder, which is part of the steganographic system, accepts the cover image, the message to be concealed, and an optional key to produce a stego image that is similar to the cover image in terms of human visual systems (HVS).
The encoder uses either a substitution technique or a transform technique to carry out the steganographic data concealment process. The cover may be broadcast via a channel or posted online.
The decoder extracts the hidden message from the stego image at the other end using the optional key.
Figure 3 Steganographic model
The following section covers over the LSB, DCT, DFT and DWT.
4.1 The Least Significant Bit (LSB) data hiding
The message was hidden via LSB steganography, which utilized the cover media's digital data's least significant bits.
LSB substitution is the most straightforward LSB steganography approach. The final bit of each data value is reversed in LSB replacement steganography to reflect the message that has to be hidden. Analyze an 8-bit
grayscale bitmap image where each pixel is represented by a single byte that corresponds to a certain grayscale value.
Assume that the first eight grayscale values of the original image are as follows [4]:
11010010 01001010 10010111 10001100 00010101 01010111 00100110 01000011
The LSBs of these pixels are changed to have the new grayscale values listed below in order to cover the letter D, whose binary value is 01000100.
11010010 01001011 10010110 10001100 00010100 01010111 00100110 01000010
Only around half of the LSBs were often required to modify. The cover image and the stego image are barely distinguishable from one another. One of its main drawbacks, though, is the tiny amount of data that can be contained in this kind of image utilizing LSB. Attacks on LSB are quite likely to succeed. In contrast to 8 bit formats [2],[8], LSB techniques implemented to 24 bit formats are difficult to identify. Another illustration of the LSB method is: Assume that the number 300 is to be embedded using the LSB approach in a grid of 3 pixels of a 24 bit picture. The grid that results is as follows:
(01010101 01011100 11011000) (10110110 11111100 00110100) (11011110 10110010 10110101)
The following bits are embedded into the above grids. 01000100
(01010100 01011101 11011000) (10110110 11111100 00110101) (11011110 10110010 10110101)
In this case, the first 8 bytes of the grid contained the number D. According to the attached message, only the three bits need to be altered. When selecting the maximum cover size, an image just has to have half of its bits changed in order to conceal a secret message.
4.2 Discrete Cosine Transform (DCT) method
JPEG compression employs DCT coefficients [10],[12]. It divides the image into various, significant portions. It changes a signal or image's frequency domain from the spatial domain. The image is divided into components with high, middle, and low frequencies. While high frequency sub band often removes high frequency components of the image by compression and noise attacks, low
frequency sub band has a large portion of the signal energy at low frequency, which retains the majority of the visual components of the image [13]. In order to ensure that the image's visibility is unaffected, the secret message is therefore inserted by changing the middle frequency sub band's coefficients.
Figure 4 DCT of an image
The general equation for a 1D (N data items) DCT is defined by the following equation [12]:
(1) Where u = 1, 2, 3…..N-1. The general
equation for a 2D (N × M image) DCT is defined by the following equation [12]:
(2) Where u, v = 1, 2, 3…..N-1. The input image in this case is N M in size. C (u, v) is the DCT coefficient in row u and column v of the stego picture, and c I j) is the intensity of the pixel in row I and column j. Using MSE [2], the image distortion may be quantified. Matrix DCT.
8x8 pixel blocks make up a DCT image.
DCT is applied to each block, moving from top to bottom and left to right. To scale the DCT coefficients and embed the message, each block is compressed using a quantization table [10].
4.3 Discrete Cosine Transform (DFT) Method
We are aware of the fact that the equation for the two-dimensional discrete Fourier transform (DFT)[19,20], which is provided by the following equation, can be used to represent an image (f(x,y) of size M x N in the frequency domain (F(u,v)).
F (u, v) = y) e-i2Π
(ux/M+vx/N) (3)
Fig. 5 DFT representation of an image The Fourier transform is based on the idea that any waveform may be created by adding sine and cosine waves of various frequencies. The variables u and v determine the frequencies at which the exponential in the previous formula can be expanded into sines and cosines. The following equation provides the discrete Fourier transform's inverse:
f (x, y)
=(1/MN) v) ei2Π
(ux/M+vx/N) (4)
As a result of the above inverse relationship, we may infer that if we have F(u,v), we can use the inverse discrete Fourier transform to produce the matching image (f(x,y)). The Fourier transform's values are complex, which means they contain both real and fictitious components. I stands for the imaginary portions and is only defined by the fact that its square is 1, or i2 = -1.
In this research, we use discrete Fourier transformation to create a colour image steganographic system. In our system, we first determine the secret message's length before converting it to ASCII format. Next, we use DFT to transfer the cover image from its spatial domain into its frequency domain by applying the equation (3). We only incorporate our secret information into the real sections of the Fourier transform because we are aware that they are complex and contain real and imaginary elements. Once the secret information has been fully inserted, we take the Inverse DFT and apply equation (4) to obtain the stego-image.
Once more, to determine the length of the secret message, inverse DFT is applied to the stego-image. The real coefficients are then extracted from the DFT representation up to the length of the message.
4.4 Discrete Wavelet Transform Technique (DWT)
This method makes use of the Haar DWT [18]. Two operations make up a two- dimensional Haar DWT: the horizontal operation and the vertical operation.
Below is a description of a 2D Haar DWT's specific steps:
Step 1:
Horizontally scan pixels in a left to right orientation. Next, apply addition and subtraction operations to adjacent pixels.
As seen in figure 6, place the sum on the left and the difference on the right.
Continue doing this until all rows have been processed. The original image's high frequency portion is represented by the pixel differences, while the low frequency portion is represented by the pixel sums (denoted by the symbol L) (denoted as symbol H).
Figure 6 The horizontal operation on first row
Step 2:
Vertically scan the pixels from top to bottom. As shown in Figure 7, perform addition and subtraction operations on nearby pixels before storing the sum on top and the difference on the bottom.
Continue doing this until all of the columns have been processed. In the end, 4 sub bands—LL, HL, LH, and HH—were discovered. Since the LL sub band represents the low frequency part, it resembles the original image quite closely.
The entire process is known as the first order 2D Haar DWT.
Figure 7 The vertical operation 5 Error Metrics
The performance analysis makes use of the following two error measures.
5.1 Peak Signal to Noise Ratio
It is the ratio of the maximum signal to noise in the stego image [5][23][24].
Decibels are used to measure PSNR (dB).
For comparing restoration outcomes for the same image, PSNR is an effective metric.
5.2 Mean Square Error
The difference between the cover image and the stego image is squared to calculate MSE[2]. Using MSE, the image distortion may be quantified.
M and N are the number of rows and column in the input image.
6 EXPERIMENTAL ANALYSIS
Three pictures formats—BMP, JPEG, and TIFF—have been used in this experiment.
Five secret messages with sizes ranging from 5 KB to 25 KB have been inserted into each of these formats, and their related MSR and PSNR have been assessed. The MSE and PSNR for various picture formats and message sizes are shown in the following table.
Table 1 Comparison between steganography using LSB, DCT, DFT and DWT Image
Format Image Size (KB)
Message Size (KB)
MSE PSNR
LSB DCT DFT DWT LSB DCT DFT DWT
BNP 191 5 0.357793 0.04324 0.05786 0.091938 51.9213 61.897 60.7841 57.9858 BNP 191 10 0.683061 0.12334 0.159406 0.366474 48.923 57.123 55.9808 53.5238 BNP 191 15 1.03992 0.4054 0.427084 0.558421 46.9867 53.021 51.975 50.7238 BNP 191 20 1.37894 0.80735 0.868154 0.786445 47.0832 49.321 48.9584 49.3387 BNP 191 25 1.9905 1.89452 1.95342 1.20272 41.0765 45.233 44.9829 46.9878 JPEG 100 5 0.349205 0.2031 8.02293 0.244181 51.9987 55.456 38.9785 53.9359 JPEG 100 10 0.705869 0.4012 8.02099 0.413995 48.8506 53.076 38.9779 51.978 JPEG 100 15 1.03946 0.6542 8.05288 0.689771 48.057 51.012 39.1013 48.9318 JPEG 100 20 1.40941 0.9001 8.08527 0.933584 47.5025 50.013 38.9939 48.5283 JPEG 100 25 1.907634 1.0421 8.23471 1.12332 40.9587 48.025 38.8934 48.0258 TIFF 206 5 0.347866 0.1393 0.0628014 0.166785 52.7167 54.987 59.9587 55.8697 TIFF 206 10 0.706157 0.4994 0.175939 0.485681 48.8537 50.878 54.9872 52.0763 TIFF 206 15 1.039500 0.7996 0.395615 0.745367 47.9459 48.827 52.0976 48.9844 TIFF 206 20 1.400800 1.3023 0.899987 1.40609 47.6981 48.564 49.4460 47.6507 TIFF 206 25 1.804500 1.7956 1.989640 1.797 42.6346 45.587 46.1409 46.5612
7 CONCLUSION
A research and implementation of colour image steganography employing LSB, DCT, DFT, and DWT have been attempted in this paper. We mainly employed the two parameters MSE and PSNR to analyze the results. We calculated these values for three picture formats and added five secret messages with sizes ranging from 5 KB to 25 KB to each image format. In comparison to DCT, DFT and DWT, LSB provides high MSE and low PSNR for all picture formats and message sizes. When we examine the MSE and PSNR for DFT and DWT, we discovered that DFT performs better than DWT for smaller message sizes (up to 25 KB), giving lower MSE and higher PSNR, whereas DWT performs better for larger message sizes.
However, because JPEG is a sort of compressed image and DWT is a method of image compression, JPEG200 uses
DWT to compress the image, DWT and DCT performs better than DFT for all message sizes in the case of JPEG image format. Therefore, we can conclude that DWT is a preferable alternative for steganography for large payloads.
Acknowledgement
The author(s) expresses their deep sense of gratitude towards all the authors whose references are being used as the source of data and idea to develop this research work. This work will act as the foundation for everyone to have a brief idea about the different techniques in watermarking.
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