Journal of Information Assurance and Security. ISSN 1554-1010 Volume 11 (2017) pp. 170-178 © MIR Labs, www.mirlabs.net/jias/index.html
Improved and Secure Differential LSB Embedding Steganography Muhammad Zaheer1, Ijaz Mansoor Qureshi1, Atta-ur-Rahman2, Jamal Alhiyafi2, Zeeshan Muzaffar3 1Department
of Electrical Engineering, Air University, Islamabad, Pakistan {mzaheer, imqureshi}@au.edu.pk 2CCSIT, Department of Computer Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, KSA {aaurrahman, alhiyafi}@iau.edu.sa 3Barani Institute of Information Technology (BIIT), Rawalpindi, Pakistan
[email protected] Abstract: With the growth of information and communication technologies and applications, requirement for the information assurance and security enhancement in data is rising exceptionally. Image steganography is one of the potential solutions for it. Least Significant Bit (LSB) based steganography is categorized as a popular and widely utilized technique in spatial domain. Conventional methods applied in LSB based steganography mostly focus on increasing the capacity of embedded information and imperceptibility while the problem of security still needs to be considered because the LSB embedding is vulnerable to several common attacks on the data like Additive White Gaussian Noise (AWGN) attack, geometric attacks and much more. The proposed work provides an innovative approach to enhance the covert transmission aspect of the system. The secret information is pre-processed by using latest devised right translated gray substitution box which maps the information to a new format. This process enhances the security of the information as data retrieval is impossible without the knowledge of mapping rule and secret key. To further emphasis on correct retrieval Bose–Chaudhuri– Hocquenghem (BCH) error correcting codes are used. Differential embedding to the LSBs of chaotically selected pixels is another step to make detection difficult and avoid error to propagate. The chaotic selection of pixels is optimized by using Genetic Algorithm (GA) which is a way forward to increase Peak Signal to Noise Ratio (PSNR) of the cover image. Simulation results reveal authenticity of the proposed scheme. Keywords: Image Steganography, Information hiding, Secret Communication, Cryptography, AES, Differential Embedding, GA
I.
Introduction
With the growing information and communication technologies, the need for secure communication is rising exponentially [1]. The aim of secure communication is that the information should reach to the intended user and should not be vulnerable or decodable by unintended users. Cryptography and steganography are the basis of secure communication. Both technologies are used to conceal secret information [2-3]. Cryptography distorts the information which can make attacker inquisitive while the steganography is utilized to ensure that the secret information should not be detectable. A better and sensible approach is to hide information in some other media to make it undetectable by an intruder. Steganography is also called covered writing. Steganography make use of cover media including image, audio or video for covert communication [4-5].
There are various embedding techniques proposed in literature. All techniques utilize variety of schemes to alter the cover image but the objective of all methods is to maximize the capacity of stego image. More precisely the focal point is to embed maximum information while making sure that the embedding is not noticeable (imperceptible) [6]. Spatial domain techniques work on varying the parameter of cover image so that the cover and stego image are nearly indistinguishable and the modification is not observable at all through the naked eye [7]. Rener et al. [8] and Ramaiya et al. [9] presented the idea of simple LSB image steganography combined with DES preprocessing of secret information. The simulation results presented that the secret information is invisible and incomprehensible because of this proposed combination. Kamadar et al. in [10] presented the performance evaluation of LSB based image steganography. The proposed criteria for performance evaluation are mean square error (MSE) and PSNR. Results depict high PSNR and low MSE achieved because of proposed algorithm [11]. This paper proposes secure communication problem based on encrypting secrete binary information using right translated Advanced Encryption Standard (AES) gray Sboxes presented by Khan et al in [12] combined with image steganography. Furthermore, the binary information which is the secret message, is given a cover of BCH error correction codes to ensure accurate detection. The preprocessed secret information is embedded in chaotically selected pixels whose selection is optimized by GA using differential LSB embedding. The combination of encryption, error correction code and optimized chaotically embedding through differential LSB encoding make the secret transmission of data incomprehensible and invisible. The proposed combination also enhances the anti-detection performance of system and increases the PSNR of cover image. Muzaffar et al. [13] presented a unique way to embed secret information in digital audio cover using changing slope method in time domain. The data was embedded in terms of the slope variation in the successive samples. The scheme was promising in terms of capacity and imperceptibility. However, it was not robust against even very friendly attacks like Additive White Gaussian Noise (AWGN) and Salt and Pepper noise because these noises may cause a jitter in the specified successive samples and the slope can easily be changed. In [15] has employed a weighted pattern matching (WPM) technique in wavelet domain for digital audio MIR Labs, USA
Improved and Secure Differential LSB Embedding Steganography steganography. In this technique, the data was embedding in wavelet domain hence; the technique was robust against certain attacks. Although, the technique was promising in terms of imperceptibility but the capacity was not the prominent feature of this scheme. Applications of binary error correcting codes (BECC) like Residue number system (RNS) and redundant RNS (RRNS) for sake of information security and integrity in digital image steganography/watermarking has been done in [15, 16, 17, 18], where Chinese remainder theorem (CRT) was used for recoding. Similarly, concatenated codes have also been investigated for sake of information security in the paradigm of image steganography and watermarking in [19, 20, 21, 22]. The main feature of these codes was to provide guard against the errors. Hence, they played a vital role in ensuring robustness against attacks. However, the main issue with these multilevel codes, was the decoding computational complexity that was significant at receiver side. In short, their scheme was suitable for offline systems but not for the real time systems.The paper is organized as follows. Section II illustrates the proposed improved and more secure LSB embedding method focusing on the significance of right translated S-Box technique, BCH encoding, Chaos, Genetic algorithm and Differential LSB embedding method. Section III presents the experimental results demonstrating the improvement in PSNR and minimizing mean square error for different cover images. Finally, conclusion and future work are presented in section IV.
II.
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Figure 1: Proposed Model The proposed work can be described in two modules. 1) Secret information pre-processing 2) Embedding Method 1) Secret Information Pre-Processing: The binary information to be transmitted is pre-processed to increase the non-comprehensible feature of the information. The information processing module is a combination of two sub modules. a) Right translated gray box b) BCH channel encoder a) Right translated gray S-box: This idea is presented by Khan et al [12]. The proposed idea is based on application of right translation and gray codes over the widely used advanced encryption standard S-Box. The proposed substitution box given in [12] consists of three types of transformations. 1. 2. 3.
Advance Encryption Standard (AES) SBox Right translation (fig-2) Gray Code 8-bit Binary Information
Proposed Model:
The overall idea of proposed method and the approaches used, is shown in Figure 1:
AES SBox
Secrete Binary Information
Chaotically selected translated SBOX
XOR
Secret Key 1
Chaotically generated binary 8bit vector
Gray Code Encoder
8-bit Encrypted Information
2-bit error correcting code (15,7)BCH encoder
Figure 2: Right Translated Gray S-Box The AES S-Box is a bijective transformation applied on 8-bit data defined as:
Cover Image
Differential LSB embedding in chaotically selected pixels
Stego Object
Optimized Secret Key 2 by GA
.
SAES (x) = {
𝑏𝑖𝑓𝑥 = 0 𝛾°𝜐(𝑥)𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
(1)
Where b = [1 1 0 0 0 1 1 0]T and x is the binary input.𝜐 is a group automorphism on elements of x defined as 𝜐 = x-1 (multiplicative inverse). 𝛾 is an affine transformation on element y defined as: 𝛾(y) = My + b. where:
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M
10001111 11000111 11100011 = 11110001 11111000 01111100 00111110 [0 0 0 1 1 1 1 1]
Previously embedded bit
Right translation is applied to the binary output of AES S-Box. Right translation is done by chaotically generating a vector and this vector is XORed with the AES binary output. This adds on to the security of the system as this chaotically generated random vector is based on information known to the intended user only. The generation is same as discussed in section 2. Finally, the gray code is calculated against the translated output using relation: 1 − 𝑏𝑛 𝑖𝑓𝑏𝑛−1 = 1 gn = { 𝑏𝑛 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Current information bit to be embedded
Is information bit == 1
No
Inverter Yes
(2)
Overall representation of right translated S-Box is given by: ζ(g) = SAES ° ρg °G
(3)
Simulation results presented against this S-Box in [4] indicated that the proposed S-Box has better resistance against computational attacks and the algorithm is designed so carefully that all newly generated S-boxes preserve all the cryptographically important properties including nonlinearity, bit independence and strict avalanche, linear approximation and differential approximation of the original AES S-box. b) BCH encoder: Error correction codes are widely utilized in communication to ensure error free data transmission. There are different categories being utilized like block codes, cyclic codes, convolution codes, Reed Solomon (RS) codes and BCH codes etc. All above mentioned techniques are categorized as forward error correction code in which decoder has the capability to detect and correct errors. The encoder is provided with k-bits data corresponding to which it generates n bit code where n is greater than k. The (n-k) redundant bits are utilized to correct error. We have utilized BCH codes in this paper. The binary output of the translated S-Box is passed to (15,7) BCH channel encoder. The encoder divides the binary bits provided to 7-bit chunks and generates an output of 15 bits corresponding to each 7-bit chunk. The code can correct up to two-bit error. This is used for demonstration in this work, however, longer codes may also be used.
Current bit to be embedded = previous embedded bit
Current bit to be embedded = (previously embedded bit)’
Figure 3: Differential LSB Encoding
2) Embedding Method: The embedding method includes LSB based image steganography combined with chaotic pixel selection and differential encoding. So, embedding method is described by using two sub modules. a) Optimized Chaotic Pixel Selection b) Differential Encoder (fig-3)
a) Optimized Chaotic Pixel Selection: This module chaotically locates the pixels which are to be used for information embedding. The chaotic selection is done by using following equation. (4) where “r” is a bifurcation parameter and for system to be chaotic and r can be between 3.57 and 4 and xo is an initial condition having value between 0 and 1. Chaotic systems exhibit numerous characteristics that distinguish it from the other systems. One feature is its sensitivity to the initial conditions which if slightly modified, an entirely different pattern is obtained.
Improved and Secure Differential LSB Embedding Steganography
Start
Initialization
(r,xo)1
(r,xo)2
(r,xo)N
Chaotic Key generator
Key 1
Encrypted Message
Key 2
So, to generate the exact chaotic sequence, exact information about xo and r is compulsory. An array of chaotic integers is generated between 1 to M x N. M x N is the size of cover image. These integers are basically the positions of pixels which are utilized to embed information. This selection is optimized by using heuristic optimization algorithm GA as shown in Fig. 4. The aim of using GA is to chaotically select the pixels in a way that the mean square error between the cover image and stego image is minimized. Genetic Algorithms are the heuristic search and optimization techniques that mimic the process of natural evolution. Genetic Algorithms implement the optimization strategies by simulating evolution of species through natural selection as shown in Fig. 5.
Key N
Differential LSB Embedding
Differential LSB Embedding
Differential LSB Embedding
Fitness 1
Fitness 2
Fitness N
GA Operators
Figure 5: Natural evolution of species
Next generation of (r,xo)1 , (r,xo)2 ….(r,xo)N
No of generation s> maxGen
Best Solution
End
Figure 4: GA Optimization
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Figure 6: Basic flowchart of GA There are three basic GA operators: Selection Crossover Mutation
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The selection process determines which solutions are to be preserved and allowed to reproduce and which ones deserve to die out. The primary objective of the selection operator is to emphasize the good solutions and eliminate the bad solutions in a population while keeping the population size constant. A fitness function value quantifies the optimality of a solution. The value is used to rank a particular solution against all the other solutions. A fitness value is assigned to each solution depending on how close it is actually to the optimal solution of the problem The most popular crossover selects any two solutions strings randomly from the mating pool and some portion of the strings is exchanged between the strings. A probability of crossover is also introduced in order to give freedom to an individual solution string to determine whether the solution would go for crossover or not (Fig-7).
Stego Object
Data Extraction from LSBs of chaotically selected pixels and differential decoding
Secret Key 2
2-bit error correcting code (15,7)BCH decoder
Chaotically selected translated SBOX inverse
Secret Key 1
Figure 7: Crossover Process Binary Information
Mutation is the occasional introduction of new features in to the solution strings of the population pool to maintain diversity in the population. Though crossover has the main responsibility to search for the optimal solution, mutation is also used for this purpose. The basic GA algorithm is shown below.
Figure 8: Basic GA Algorithm In our case, the fitness function of GA is PSNR of the stego image and the GA generates the initial conditions of the chaotic sequence which is shown in detail in Fig. 4.This in return maximized the PSNR of the stego image making the secret message more secure and invisible. When the pixels have been chaotically located, their values are converted to 8-bit binary format for embedding the information in the LSB of selected pixels. Decoding of the proposed algorithm is exactly opposite to encoding procedure as shown in Fig. 9.
Figure 9: Decoding Process
III.
Simulation Results: The proposed algorithm was tested using three different cover images. Fig. 10(a) shows the original flower image and fig-10(b) shows the image after embedding. Fig. 11(a) shows the original baboon image and Fig. 11(b) shows the image after embedding. Fig. 12(a) shows the original tree image and Fig. 12(b) shows the image after embedding. Fig. 13(a) shows the original man image and Fig. 13(b) shows the image after embedding. Table 1 shows the results achieved using the proposed model for four different cover images without using GA optimization. Table 2 shows the results achieved using the proposed model for four different cover images using GA optimization. Simulation results show low MSE between cover image and stego image. Moreover, high value of PSNR further elaborates high imperceptibility. Comparison of Table 1 and Table 2 elaborates the significance of using genetic algorithm depicting improved PSNR and minimum MSE. Following equations describe the MSE and PSNR respectively. 𝑀𝑆𝐸 =
∑𝑀,𝑁[𝐼1 (𝑀,𝑁)−𝐼2 (𝑀,𝑁)] 𝑀∗𝑁
𝑃𝑆𝑁𝑅 = 10𝑙𝑜𝑔10
𝑅2 𝑀𝑆𝐸
(5) (6)
I1 is original whereas I2 is stego image. M and N are number of rows and columns in input image respectively.
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(a) (a)
(b) Figure 9: Flower Image (a) Original (b) Stego (b) Figure 11: Tree Image (a) Original (b) Stego
(a) (a)
(b) Figure 10: Baboon (a) Original (b) Stego
(b) Figure 12: Man (a) Original (b) Stego
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Table 1: PSNR of different cover images without using GA optimization Cover Image
Cover Image Size
No of Secret Inform ation Bits
No of Bits Embedded After PreProcessing
MSE
PSNR (dB)
Flower
192 X 262
112
240
0.0023
74.22
Baboon
138 X 138
112
240
0.0060
70
Tree
192 X 262
112
240
0.0024
74
Man
209 X 153
112
240
0.0040
72.11
Table 2: PSNR of different cover images using GA optimization Cover Image
Flower Baboon Tree Man
Cover Image Size 192 X 262 138 X 138 192 X 262 209 X 153
No of Secrete Informati on Bits 112 112 112 112
No of Bits Embedded After Pre Processing
MSE
240 240 240 240
0.00011 0.00031 0.00013 0.00018
PSNR (dB)
Figure 13 (a): Original MRI Image 87.71 83.21 86.99 85.57
In addition to the results shown above, we have also utilized the algorithm in medical domain. The steganography technique is widely used to embed secret message in the medical images like CT, Ultrasound and MRI images. The secret message can be the patient’s particulars and some diagnosis can also be added. The steganography is considered to be a potential candidate to embed these particulars as this is considered to be a very confidential information. For sake of investigating the applicability of the proposed scheme for the medical images, we have considered two different medical images for simulations. First is a 512 x 512 MRI image of a brain and the other is 300 x 225 abdominal ultrasound image. We have embedded 7 bytes for patient’s last name and 7 bytes for MRI number in these images. The information has been prep-processed in a similar way as discussed above, so if the intruder even detects the information in the image it is very hard to decode the original information without knowing the encryption and channel coding. Fig. 13(a) shows the original MRI image and Fig. 13(b) shows the stego image after embedding the secret information. Fig. 14(a) shows the original ultrasound image and Fig. 14(b) shows the stego image after embedding the secret information.
Figure 13 (b): Stego Image
Figure 14 (a): Original Ultrasound Image
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yielding higher values of PSNR and minimum MSE. The super imposition of BCH codes is an additional step towards security improvement making the secret information barely visible and hard to interpret. Simulation results illustrate high PSNR and low MSE value depicting high imperceptibility achieved by the proposed algorithm. To explore transform domain for the described embedding and encryption techniques to improve the robustness of the stego image is our future work. References
[1] Figure 14 (b): Stego Image
[2]
Table 3: Simulation Results for medical images using GA Cover Image
Cover Image Size
No of Secrete Information Bits
No of Bits Embedded After Pre Processing
MRI
512 X 512
112
240
Ultrasound
300 X 225
112
240
PSN R (dB)
77.3 3 72.8 4
[3]
[4]
[5] Table 4: Simulation Results for medical images using GA Cover Image
MRI Ultrasound
Cover Image Size
No of Secrete Information Bits
No of Bits Embedded After Pre Processing
PSNR (dB)
512 X 512 300 X 225
112 112
240 240
88.40 82.13
It can be seen in Table 3 that the medical images after embedding the secret information possess high PSNR indicating high imperceptibility which is the key feature of steganography. Table 4 presents further enhancement in the results after the incorporation of GA. The noticeable increase in PSNR shows the effectiveness of using this tool. The incorporation of the encryption and error correction codes makes it a perfect choice to be utilized for embedding confidential patient data or diagnosis in medical images.
IV.
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Naitik P Kamdar, Dipesh G. Kamdar, Dharmesh N.Khandhar, “ Performance Evaluation of LSB based Steganography for Optimization of SNR and MSE”. Journal of Information, Knowledge and Research in Electronics and Communication Engineering Volume 2
[11]
Daemen J, Rijmen V. “The Design of RIJNDAEL: AES —the Advanced Encryption Standard”. Springer- Verlag: Berlin, 2002.
Conclusions The proposed work primarily exploits image steganography with a blend of right translated gray Sbox and chaotic differential LSB embedding. The chaotic embedding is further aided by genetic algorithm (GA) to minimize the distortion in cover image by shuffling the positions of the pixels to embed the information and selecting the optimized locations
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