evolved algorithm to secure communication with ...

IJICIS, Vol.17 No. 1 JANUARY 2017

International Journal of Intelligent Computing and Information Science

EVOLVED ALGORITHM TO SECURE COMMUNICATION WITH STEGANOGRAPHY S. A. Shawkat

O. Abu-Elnasr

T. Elarif

Computer Science Department, ‎Faculty of Computers and ‎Information, Mansoura University - Egypt [email protected] [email protected]

Computer Science Department, ‎Faculty of Computers and ‎Information, Ain ‎Shams University - Egypt [email protected]

Abstract: Steganography refers to the science and art of hidding information inside a carrier, where nobody except the intended recipient, knows about the ‎existence of hidden information. The hidden information can be in a form of text, audio, image or video. The procedure approach conceals the secret text relied on seeking for the congruent bits between the secret text and image pixel values. The main objectives of this work were to develop a security system for securing visual in addition to statistical offenses and to improving the finesse of received data. The proposed system was developed based on the Least Significant Bit (LSB) approach as a common steganography technique and 2D Discrete Wavelet Transform (DWT) to develop 2-level Discrete Wavelet. Digital images are the commonly utilized cover files due of their frequency on the internet, however, numerous file formats can be used for that propose. In this work,the possibility of embedding data(secret message) in the frames of image files and enhance secrecy of the information and develop an avenue for secure data transmission.The least significant bit of binary differences and 2D-DWT 2-level were optimized using the ASCII encoding format. The Haar-DWT was used as the iteration domain transforms. A 2D HaarDWT was composed of two transactions: one is horizontal operation and the other is vertical. Crossreference analyses using different formats of cover images were carried out in this work. Also, the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Bit Error Rate (BER)were used as functional evaluation parameters. These evaluation parameters indicated that our proposed approaches have achieved significant enhancement in securing hidden data. Keywords: Steganography,LSB techniques, DWT2-level techniques,Haar-DWT, Steganography, Peak Signal-to-Noise Rate (PSNR), Mean Square Error (MSE), Bit Error Rate (BER). 1. Introduction The consequence of critical data, concealing concerns the minds of people, particularly in business, military and political sphere regarding the confidentiality of their data. Accordingly, there is a consistent need to develop approaches that are used in transmitting such data through the internet. Therefore, secure communication throughout unsafe channels is likewise the Internet has become a hotspot in the research sphere once the initiated of the digital era [1]. In this work, a security approach was developed to be used in transmitting classified data to the receiver’s side. According to this approach user can chose an image file for data concealing and save it as the output file. So memorize the output image file will involve the secret message concealed inside it. 1

Shawkat Et Al: Evolved Algorithm To Secure Communication With Steganography

The receiver will capable of reading the secret message after decoding the image file including the concealable data. The steganography image can be used in safely transmitting data via unsafe transmission channels, whichever the transmission protocol utilized (i.e., email, stream, file sharing, P2P networks, etc.). The concealing context is hard to distinguish. This is because images are part of the more popular transmitted media varieties on the Internet, which make them an exemplary cover file for secret data transmission [2].

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Receiver

Sender

In steganography scheme the secret message, which is called plain-text is concealed in a cover file to create what is called a stego-carrier. Concealing information probably demands a stego-key which is Supplementary secret information, likewise a password, demanded for implantingthe information. For instance, when a secret data is concealed inner a cover image, the outcome product is a stego-image.The cover medium is any digital medium like image, audio or video. The most illustrious steganographyapproach that operates the spatial domain is the least significant bits (LSB). The LSB is an initial of the broadest and planer approaches utilized in image steganography. The benefit of steganography is that it can be utilized to transmit classified messages without the fact of the transmission being detected. Often, utilizing encoding might identify the sender or receiver as somebody with something to conceal.Concealed data in images using encoding is significantly sensible to image reinstatement and at-risk to offense. Desegregated analysis of the LSB could be found in [3][4][5][6]. Figure1 shows a block diagram of a generic steganography mechanism.

Extracting

Figure 1: A Block diagram of a generic steganography mechanism.

The abovementioned figure illustrates a scheme of the generic embedding and extraction mechanism in steganography.DWThas great spatial localization, frequency spread and multi-resolution characteristics, which are matching with the theory of forms in the human visual system [7]. A 2D level of DWT is implemented to split up the image into low frequency and high frequency parts [8]. The DWT divides the image into high and low iteration parts. The high iteration part contains edge information, whereas the low iteration part is dividedfrequently into high and low iteration parts. The Level-1 DWT decomposes an image into a lower resolution image (LL1) likewise horizontal (HL1), vertical (LH1) and diagonal (HH1) parts. For Level-2 disintegration, the DWT algorithm is repeatedly applied on the LL1 part which is further disintegrated into four sub-bands LL2, HL2, LH2 and HH2. In general, a secure transmission involves four steps: concealing, sending, receiving and uncovering. This is a twostep operation: the first step is to block the concealed data as it is transmitted from the sender to the receiver and the second step defines the concealing of this data [9]. 2


This paper is organized in five sections including this one. Section 2 describes the related work; section 3 discusses the proposed techniques and their algorithms; section 4 shows the experimental results and their discussions; and section 5 summarizes the main conclusions and suggestions for future work. 2. Related works The concern of utilizing images such cover media for steganographic transmission was rising in the last decades. The word steganography originates from the ancient Greek words steganos (covered) and graphic (writing), "Covered Writing". It has been utilized in different models for the great many of years. In the fifth century BC, Histaiacus shaved the leader of his messenger, wrote the ‎message on his scalp, and then waited for the hair to grow again. The messenger, obviously carrying nothing pugnacious, could transmit freely. When he gets at his destination, he shaved his head and the secret message could be simply read by the receiver [10-11]. One of the ancient approaches to conceal a message inner a text is to take the first letter of per word. Because everyone can read, encoding text in neutral-sentences is doubtfully effective. By taking the first letter of per word we get the secret message [12]. After that, the Germans produced a ‎ mechanism called the microdot. Microdots are photograph with the measure of a printed period, but however ‎a whole page of information. The microdots where imprinted in a letter or on an envelope and being so ‎small, they couldn’t seen suspicious [13]. In this work, the authors proposed a newly mechanism to enhancethe security and the ingenuity of the image steganography. The mechanism integrated a variation of the plain ‎LSB algorithm by using the bit reversal approach. The feature of the mechanism were to diminish the ‎number of altered bits, produced a high artfulness of stego-image and enhance the image ingenuity and the ‎robustness [14].LSB indicates the right-hand bit in a binary integer; where LSB replaces the fewer bits in images with secret message bits. LSB algorithm is the ordinary and handy manner for concealing information. This algorithm supplies a rise implanting capacity, lineamentand declines computational power of steganography [1516]. Satar et al.[11] proposed a steganography approach for sharing the mystery message by using multicover adaptive steganography. In their methodology, the mystery ‎message was embedded into LSB plane and 2LSB plane of the cover image.‎‎Thisapproach shall apply XOR and AND operators to the secret message bit and the image. They made an assessment of their suggested approach by calculating the structural similarity index (SSIM) and PSNR. They concluded that the suggested approach has fulfilled the subsequent demands; high finesse shares, lossless reconstruction and computational efficiency. A research presented by Akhtar et al. [17], made an improvement in the plain LSB based image steganography through using the bit-inversion technique to improve the quality of stego-image. They implemented two schemes of the bit-inversion techniques. In these techniques, LSB of some pixels in the cover image were inverted when they occur with a particular pattern of some bits of the pixels. In this method, less number of pixels are modified in comparison to the plain LSB method and the PSNR of stego-image is also improved. In order to correct de-steganography, the bit patterns for which LSBs has inverted, needs to be stored within the stego-image somewhere. The proposed technique showed good improvement over the plain LSB steganography. It can also be integrated with other approaches to further improve the steganography. Kaur et al. [18] have proposed a spatial domain technique (i.e. LSB) for image steganography. In this approach data are hidden in combination of LSBs instead of only hiding the data in least significant one bit. They have used these combinations of bits: LSB (1, 2) bits and (2, 3) bits. They have compared their results using qualitative and quantitative parameters (i.e., PSNR, MSE, BER, Entropy, Standard deviation). They found that hiding the data in (1, 2) bit pair was better than hiding it in (2, 3) bit pair. 3


Reddy and Raja [19]suggested a steganographyapproach in which information is concealed into a publicly accessed color image by a quantization-based strategy. Accordingly, the transportation of the secret information will not attract the attention of illegal eavesdropper. With this approach, the secret information is implanted in the wavelet domain of every chrominance component. Consequently the concealment capacity is larger than the similar steganography software. The implanted sequence can be reliably extracted without resorting to the original image.Nag et al.[20]suggested a gray image steganography based on the DWT algorithm. The DWT was used to transform the original image (cover image) from the spatial domain to frequency domain.At first, 2D DWT wascarried out on a gray-scale cover image of size m×n and Huffman encoding wasgenerated on the secret-messages before embedding it. After that, perbit of Huffman code of secret-message wasperformed in the high frequency coefficients obtained from DWT. It was found that image finesse can be improved through preserving the wavelet coefficients in the low-frequency sub-band. Ghasemi et al. [21]proposed a novel Steganography scheme through the application of Wavelet Transform and Genetic Algorithm. They employed a genetic algorithm based mapping function to implant data in DWT coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment mechanism should be applied after implanting the message. The frequency domain was used to improve the robustness of steganography. Also, both the Genetic Algorithm and Optimal Pixel Adjustment Mechanism were implemented to obtain an optimal mapping function to reduce the difference error between the cover and the stego-image, and consequently improve the concealment capacity with low distortions.Mandal and Das[22]proposed an adaptive steganography approach based on the modified pixel-worth differencing through ‎management of pixel values inside the scope of gray scale. The proposed Pixel Value Difference (PVD)‎approach checks whether the pixel value exceeds the range on embedding positions where the pixel exceeds boundary has been marked and a delicate handle is utilized to keep the value within the range. In PVD approach pixel values in the stego-image may exceed the grayscale range which is not desirable as it may lead to improper visualization of the stegoimage. In this paper they introduced anapproach to overcome this problem. 3. The proposed approach The LSB is a common, simple approach to embed information in a cover file. The LSB is the lowest order bit in a binary value; which is an important concept in computer data storage and programming. It applies to the order in which data are organized, stored or transmitted. Hiding text in the cover image without leaving any visuable sign in it to indicate any embedded text, is called the stego-cover. The cover after hiding data in it should look the same as the original cover to a third party when displaying on the screen. The proposed approach was intended to evolve both the LSB and 2D-DWT-2Level seperately in embedding and extracting hidden data. In this work the 2D-DWT ‎schema developed by 2level Discrete Wavelet was used to get to the point of being able to perform quite robust embedding, which is very ‎difficult to break or decipher. ‎The illustrated block-schematically explains the proposed LSB approach used in data embedding algorithm ‎(1)‎ and data extraction algorithm‎ (2)‎.The blockschematically for evolving the 2D-DWT-2Level is also illustrated in data embedding algorithm ‎(3)‎and data extraction algorithm‎ (4) ‎for embedding and extracting data, respectively.The proposed ‎system utilizes the least significant bit of binary changes and 2D-DWT-2Level to optimize ‎the ASCII embedding layout.‎

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3.1 Incorporation process and recovery process by evolved LSB 3.1.1 Text incorporation procedure The LSB data embedded mechanism was improved through making a harmony between the number of bits used in embedding the concealed text and the number of bits used in embedding the cover file. The pixel is randomly selected in the cover image and accordingly the binary value to be concealable is embedded in the bits of the cover image. In this work, the secret text is transformed into an ASCII format, which subsequently is transformed into a binary format. In the extraction process to retrieve the secret text, the pixel location, number bits, and dimensions of the original image are needed to be known. Finally, the binary matrix is created from converting the cover file into binary values as a result of matching the secret text with the cover file and encoding the differences between them to obtain the stego-file. The LSB algorithm (1) used in text incorporation is desceibed in the following: Embedding Algorithm (1): Least Significant Bit Hiding Algorithm (LSB): Inputs: cover image, secret message. Output: Stego image. Begin 1. convert the secret message in ASCII Code as asciiMsg 2. convert the ASCII Code to Binary Values as binMsg 3. read N  length of binMsg 4. prepare b as zero vector of N 5. Iteration on N with index k 6. if (binMsg (k) = = '1') 7. set b(k) = 1; 8. else 9. set b(k) = 0; 10. end 11. end of Iteration 12. scan the image row by row and encode it in binary as img 13. read height of img 14. read width of img 15. Define counter as k = 1; 16. Iteration on height with i = 1: height 17. Iteration on width with j = 1 : width 18. Define flag as LSB = mod(double(img (i,j)), 2); 19. if (k>m || LSB == b(k)) 20. set img(i,j) = img (i,j); // no changes in cell 21. elseif (LSB == 1) 22. set img(i,j) = (img (i,j) - 1); 23. elseif (LSB == 0) 24. set img(i,j) = (img (i,j) + 1); 25. end 26. k = k + 1; 27. end 28. end 29. set the image with the new values and save it as img End. 5


3.1.2 Text recovery procedure After incorporating the text into the cover file (the stego-image), it will be recovered through using the recovery algorithm (2) described below. It is important to identify the stego-image to uncover the message bits, rather than the secret text noumenon. In order to generate the text message, the recipient requires to get to the chain of pattern binary matrix that was used in embedding the procedure. Extraction Algorithm (2): Least Significant Bit Hiding Algorithm (LSB): Input: Stego image. Output: Retrieved secret message, cover image. Begin 1. scan the image row by row and encode it in binary as s 2. read height of s 3. read width of s 4. Define flag m = double ( s(1:1:1) ) * 8 ; 5. Define Counter as k = 1; 6. Iteration on height with index i = 1 : height 7. Iteration on width with index j = 1 : width 8. if (k