A Robust Multilayer Image Steganography Method Based on a Randomized Lsb Algorithm

International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 9, September 2018, pp. 272–284, Article ID: IJMET_09_09_031 Available online at https://iaeme.com/Home/issue/IJMET?Volume=9&Issue=9 ISSN Print: 0976-6340 and ISSN Online: 0976-6359

A ROBUST MULTILAYER IMAGE STEGANOGRAPHY METHOD BASED ON A RANDOMIZED LSB ALGORITHM

Saad Almutairi Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Saudia Arabi

Abdulgader Almutairi College of Sciences and arts in ArRass, Qassim University, Qassim, Kingdom of Saudi Arabia

ABSTRACT Recently, the demand for the uses of the steganography techniques to exchange a hidden secret data throughout unsecure networks has increased substantially. Nonetheless, although there are many techniques and methods out there in steganography field, a further researches need to be conducted in order to come up with a new secure techniques and methods. Thus, our research proposes a new robust multilayer image steganography method called MITEGO method that is based on a randomized LSB algorithm to overcome the weaknesses of the previous related works in the field, and to provide more secure image steganography at different points. MITEGO method follows a set of rules to create a stego image object (stego file) to embed and extract a secret message based on two layers: Hardening Layer and Embed/Extract Layer. Subsequently, MiTEGOsoft software tool is developed based on MITEGO method. Finally, MITEGO method is evaluated based on experiments and analysis of corresponding results. The research calculated Peak Signal-to-Noise Ratio (PSNR) in decibel (dB) unit by using ImageMagick software tool for each generated stego file. The obtained results are compared to the previous related works to prove the significant of our designed MITEGO method. This research achieves a significant improvement by up to 15.46% in the area of image steganography. Key words: Steganography, Image Steganography, Hiding Data, Extracting Data, LSB Algorithm, Spatial Domain, Transform Domain. Cite this Article: Saad Almutairi and Abdulgader Almutairi, A Robust Multilayer Image Steganography Method Based on a Randomized LSB Algorithm, International Journal of Mechanical Engineering and Technology 9(9), 2018, pp. 272–284. https://iaeme.com/Home/issue/IJMET?Volume=9&Issue=9

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1. INTRODUCTION Steganography is a modern hot topic that concerned with safeguarding a secret data prior a transmission between different parties through public networks. It basically looks for hiding a secret data inside another digital object in order to hide it existence before submitting it, and thus guarding it from the potential prying eyes. In general, steganography can be classified based on four types, namely text steganography, video steganography, audio steganography, and image steganography. Since the digital images are the most common objects due to their rate of recurrence on the Internet, this research focuses on image steganography [1-3]. The steganographic techniques are categorized according to a cover object alteration during hiding a secret data into: Spatial and Transform domains techniques. The former changes the cover (carrier) object’s pixels straight when hiding a secret data inside it. Such as of spatial domains techniques are Least Significant Bit (LSB) technique, Gray-Level Modification (GLM) technique, and Pixel Value Differencing (PVD) technique. The latter uses frequencies of a cover (carrier) object to hide the secret data. The most common techniques fallen in this domain are: Discrete Wavelet Transform (DWT) technique, Discrete Fourier Transform (DFT) technique, and Discrete Cosine Transform (DCT) technique [2][4- 10]. Generally, digital images involve two broad file’s formats: Raster and Vector images. Raster images contain a fixed number of rows and columns of pixels such as Joint Photographic Experts Group (JPEG), Tagged Image File Format (TIFF), Graphics Interchange Format (GIF), Windows bitmap (BMP), and Portable Network Graphics (PNG). Whereas Vector images consist of points that have both direction and length, such as Computer Graphics Metafile (CGM) and Scalable Vector Graphics (SVG) [1][8-18].

2. LITERATURE REVIEW Our previous research in [20] outlines the mandatory optimal specifications for a secure image steganography method. Firstly, it reviewed and analyzed various related researches in the field such as [1][21-23], and eventually shaped up these mandatory optimal specifications for a secure image steganography method. This research is conducted based on our previous research in [20] to propose and design a robust multilayer image steganography method based on a randomized LSB algorithm. The research considered and fulfilled all of the optimal specifications for a secure image steganography method that are mentioned in our previous research in [20] to our new robust multilayer image steganography method based on a randomized LSB algorithm as shown in Table 1 below:

Table 1 Optimal Specifications fulfillment in our new proposed MITEGO method. No. Optimal Specification in [20] Our new proposed MITEGO Method MITEGO method is designed to support a multilayer approach; 1 A multilayer approach therefore, it involves two consecutive layers: Hardening Layer and Embed/Extract Layer. MITEGO method is designed to provide a fast reasonable performance for processing image steganography by adapting spatial LSB technique to create a randomized LSB that has a fast 2 A reasonable performance performance, and avoiding transform domains techniques that cause a negative impact on performance due to their complex mathematical computations. MITEGO method proposes AES algorithm for encrypting and decrypting a secret data. Since AES uses only one private key for An encrypting a secret data prior 3 both operations: encryption and decryption, the research utilizes embedding process RSA algorithm for encrypting AES private key with RSA public key of the receiver. Therefore, the receiver needs first to decrypt the

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encrypted AES key with his RSA private key in order to get AES key, and then use it to extract the secret data [24][25][26][27]. MITEGO method proposes a randomized LSB in order to harden the traditional LSB and derivations such as LSB1 and LSB2 methods, A secure design for embedding and which is pretty easy to recover the original message. First, it gathers 4 extracting a secret data a sender’s password, and then pass it to hash function SHA-256 in order to identify and harden random locations that carry bits of a secret data [28]. MITEGO method proposes AES algorithm for encrypting and decrypting a secret data. As well, it proposes RSA algorithm for 5 An encryption and decryption layer encrypting/decrypting and distributing AES private key with two authorized parties, in addition to the hash function SHA-256 for identifying and hardening keys and random locations. MITEGO method is designed to process and manipulate all images 6 Supporting all images sizes sizes. MITEGO method is designed to support all Raster images as input images, and save the output in Portable Network Graphics (PNG) image format. The Portable Network Graphics (PNG) is a preferred Supporting all images formats 7 image format in this research because is a free and open source file (Raster Images) format, a truecolor image with and without alpha channel, and provides file integrity checking, transmission errors detection and lossless compression.

3. THE PROPOSED ROBUST MULTILAYER IMAGE STEGANOGRAPHY (MITEGO) METHOD The proposed multilayer image steganography (MITEGO) method is a new robust method for multilayer image steganography, which follows a set of rules to create a stego file (image object file). In general, a stego file is produced by embedding a secret message (msg) into a cover image (c) using a password (key). MITEGO method embeds and extracts a secret message based on two subsequent layers namely, Hardening Layer and Embed/Extract Layer, as explained in Fig. 1.

Figure 1 MITEGO Method Processes.

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MITEGO method embeds a secret message through two subsequent multilayer, namely Hardening Layer and Embed/Extract Layer, as shown in Fig. 1 (A) and Fig. 3 below, as follows:

Hardening Layer • A sender compresses a secret message (msg) using GZIP compression’s algorithm GZIPCompress(msg) to produce a compressed message (msg1). The benefit of this step is that it reduces the secret message’s size. • Then, a sender encrypts the compressed message (msg1) using AES algorithm AESEncrypt(msg1, keySender) to produce an encrypted message (msg2), where keySender is a key chosen by the sender himself. The benefit of this step is that hardens (secures) a secret message, so that only an authorized receiver with the valid key keySender can read it. At this moment, a secret message is compressed and encrypted and then it passed to the next layer in order to embed it into a cover image (C).

Embed/Extract Layer • A sender encrypts keySender using RSA algorithm with the receiver’s public key RSAEncrypt(keySender, RCVpubkey) to produce key, where RCVpubkey is a receiver’s public key. The benefit of this step is that it protects keySender, which will be sent to the receiver through the Internet, since RSA algorithm permits only the receiver to read it after he decrypts it with his RSA private key (RCVprikey). • A sender generates a hash table based on keySender using SHA-256 hash’s algorithm SHA- 256(keySender). The benefit of a hash table is that it is used to determine the appropriate random locations of secret message’s bits on the cover image, hence MITEGO method depends on a randomized LSB algorithm. MITEGO method calculates the location of this random bit, as depicted in Fig. 2 below, in order to hide a secret message using the following functions: • rand = new Random(Hash Table) • x = rand.nextInt (imgWidth) • y = rand.nextInt (imgHieght) • Channel = rand.nextInt (3) • Bit = rand.nextInt (channelBitUsed)

Figure 2 Randomized LSB Algorithm Bits. 1. A sender embeds an encrypted message (msg2) into the cover (original) image (C) according to a hash table (HT) to produce the stego file (SF). 2. Finally, a sender submits the stego file (SF) and key to the receiver.

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Figure 3 MITEGO Embed Process Flowchart. Figure 4 MITEGO Extract Process Flowchart. On the other side, MITEGO method extracts a secret message through two subsequent multilayer namely Embed/Extract Layer and Hardening Layer, as is explained in Fig. 1 (B) and Fig. 4 above, as follows:

Embed/Extract Layer: • A receiver gets the stego file (SF) and key from the sender. • A receiver decrypts key using RSA algorithm with his private key RSADecrypt(keySender, RCVprikey) to get keySender, where RCVprikey is a receiver’s private key. • A receiver uses keySender to generate a hash table using SHA-256 hash’s algorithm SHA- 256(keySender), which is used to determine the appropriate random locations of secret message’s bits on the stego file (SF). • A receiver extracts the embedded message (msg2) from the stego file (SF) based on the hash table (HT). Up to this point, the embedded message (msg2) is extracted from the stego file (SF), but is still encrypted and compressed, then passed to the next layer to get the original secret message (msg).

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Hardening Layer: • A receiver decrypts msg2 using AES algorithm with keySender AESDecrypt (msg2, keySender) to get msg1. • Finally, a receiver decompresses msg1 using GZIP compression’s algorithm GZIPdeCompress(msg) to get the original secret message (msg). Now a receiver reads the secret message. 4. DEVELOPING MITEGOSOFT SOFTWARE TOOL As well, this research developed MiTEGOsoft software tool based on our MITEGO method design. MiTEGOsoft software tool provides Embed and Extract processes according to MITEGO embed process pseudo code and MITEGO extract process pseudo code, respectively. The embed process of MiTEGOsoft software tool is developed based on the pseudo code of MITEGO embed process as shown in Fig. 5 below.

Figure 5 MITEGO Embed Process Pseudo Code. The GUI interface of MiTEGOsoft embed process, as shown in Fig. 6 below, requires various essential inputs, such as: Message File is the secret message which needs to be hidden, Cover File is the original image that is used to carry out the secret message within it, Output Stego File is the produced modified image that holds the secret message, Sender Password is a chosen password that is used for encrypting a secret message before hiding it, Destination (Receiver) Public Key is the key that is used by sender to encrypt the chosen password, and Output Encrypted Password File holds the encrypted password.

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Figure 6 MiTEGOsoft Embed Process. Similarly, the extract process of MiTEGOsoft software tool is developed based on the pseudo code of MITEGO extract process as shown in Fig. 7 below.

Figure 7 MITEGO Extract Process Pseudo Code. The GUI interface of MiTEGOsoft extract process, as shown in Fig. 8 below, requires various essential inputs, such as: Input Stego File is an image that holds a hidden secret message, Output Folder for Message File is a folder (directory) which saves the extracted message file (secret message), Encrypted Password File is a file that holds an encrypted password, and Destination (Receiver) Private Key is a key used by a receiver to decrypt the encrypted password.

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Figure 8 MiTEGOsoft Extract Process. 5. EVALUATING MITEGO METHOD Evaluating MITEGO method after the embed process has been done as follows. First, we calculate Peak Signal-to-Noise Ratio (PSNR) for each one of the three images samples according to the following equations [8][22][29]: 푀퐴푋2 푃푆푁푅 = 10. 푙표푔 ( 퐼 ) ……….…..………...... ……. (1) 10 푀푆퐸 퐵 푀퐴푋퐼= 2 - 1 ………………………………………...…. (2) 1 푀푆퐸 = ∑푚−1 ∑푛−1[퐼(푖, 푗) − 퐾(푖, 푗)]2 ………….... (3) 푚 푛 푖=0 푗=0 Where: • MSE: is Mean Squared Error. m×n monochrome image I, and K its noisy approximation.

B • MAXI: is the maximum possible pixel value of the image. B bits per sample MAXI is 2 −1. The PSNR is used to calculate an approximation to human perception of reconstruction quality, since a higher PSNR values indicates that the reconstruction is of higher quality. Practically, ImageMagick software tool is used in this research to calculate PSNR for all images samples, and to compare them with the original images. The ImageMagick software tool commands used for this function as follows: compare -verbose -metric MAE original.png stego.png difference.png compare -verbose -metric PSNR original.png stego.png difference.png Then, the achieved results are compared with the previous related works [22-23] in order to show the significance of this research as is depicted in results and discussion section.

6. RESEARCH EXPERIMENTAL ENVIRONMENT The experimental environment that is used for testing and evaluating MITEGO method includes: i. Operating System: a. Windows 10 Home 64-bit b. Processor Intel (R) Core (TM) i3-6100U CPU @ 2.30GHz, and Installed RAM is 4 GB. ii. Programming Language:

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a. Java(TM) SE Runtime Environment (build 1.8.0_102-b14) b. Eclipse IDE (Neon.1 Release 4.6.1) for Java Developers c. Our developed software tool, namely MiTEGOsoft software tool. iii. ImageMagick Tool (Version 7.0.3-4): is a software tool that is used to calculate PSNR for all stego files, and to compare them with the original images. iv. OpenSSL Tool (Version 0.9.8h-1): is an open source software, which is used for cryptographic functions implementation. It includes executable commands with cryptographic functions, such as: a. Symmetric cryptographic algorithms specifically DES and AES algorithms. b. Asymmetric cryptographic algorithms specifically RSA algorithm. c. Hashing functions specifically MD5 and SHA256. v. Standard Steganographic Images: the common special images used in image steganography are: a. Lena.png image. b. Baboon.png image. c. Pepper.png image.

7. RESULTS AND DISCUSSION The following sections present research results and analyzed them consequently. Fig. 9 below shows the original image (cover file) Lena.gif image in column a, the modified image (stego file) LENA.png in column b, and the difference between the two images Diff1.png in column c.

a) Lena.gif Original Image b) LENA.png Stego Image c) Diff1.png Image Figure 9 Lena Image Process Then, PSNR value is calculated by using ImageMagick software tool between the original image Lena.gif and the modified (stego file) image LENA.png. As shown in Fig. 10 below, PSNR value of Lena image is 56.6702 dB.

Figure 10 Lena Image PSNR Value

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Likewise, Fig. 11 below displays the original image (cover file) Baboon.png image in column a, the modified image (stego file) BABOON1.png in column b, and the difference between the two images Diff2.png in column c.

a) Baboon.png Original Image b) BABOON1.png Stego Image c) Diff2.png Image

Figure 11 Baboon Image Process After that, PSNR value is calculated by using ImageMagick software tool between the original image Baboon.png and the modified (stego file) image BABOON1.png. As shown in Fig. 12 below, PSNR value of Baboon image is 56.4533 dB.

Figure 12 Baboon Image PSNR Value Similarly, Fig. 13 below shows the original image (cover file) Pepper.jpeg image in column a, the modified image (stego file) PEPPER.png in column b, and the difference between the two images Diff3.png in column c.

a) Pepper.jpeg Original Image b) PEPPER.png Stego Image c) Diff3.png Image

Figure 13 Pepper Image Process Afterwards, PSNR value is calculated by using ImageMagick software tool between the original image Pepper.jpeg and the modified (stego file) image PEPPER.png. As shown in Fig. 14 below, PSNR value of Pepper image is 49.694 dB.

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Figure 14 Pepper Image PSNR Value Table 2 below presents PSNR values of our research in MITEGO column, and previous related research in [22]. The PSNR values of MITEGO method are calculated by using our developed software tool MiTEGOsoft, which is based MITEGO method. As presented in Table 2, the PSNR values of MITEGO method are compared to their corresponding PSNR values of the previous related works in order to show the research significance. The PSNR values of MITEGO method are compared to the highest gained results of the previous related works, which is in Method in [22] column.

Table 2 PSNR Values of this Research, and Researches in [22][23]. PSNR (dB) Image Difference in dB Improvement SLDP ESLDIP Method in [22] MITEGO Lena 40.4019 45.72295 49.1564 56.6702 + 7.5138 + 13.26% Baboon 40.0712 45.33670 47.7283 56.4533 + 8.725 + 15.46% Pepper 40.5886 45.42819 47.4422 49.694 + 2.2518 + 4.53%

70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 SLDP ESLDIP Method in [22] MITEGO Lena 40.4019 45.72295 49.1564 56.6702 Baboon 40.0712 45.3367 47.7283 56.4533 Pepper 40.5886 45.42819 47.4422 49.694

Lena Baboon Pepper

Figure 15 Chart of PSNR Values of this Research, and Research in [22][23]. As shown in Table 2 below, the PSNR value of Lena image in MITEGO method is 56.6702 dB, while it is 49.1564 dB in [22]. MITEGO method has improved PSNR value of

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Lena image by 7.5138 dB, which is equal to 13.26 %. In addition, the PSNR value of Baboon image in MITEGO method is 56.4533 dB, while it is 47.7283 dB in [22]. MITEGO method has improved PSNR value of Baboon image by 8.725 dB, which is equal to 15.46 %. The PSNR value of Pepper image in MITEGO method is 49.694 dB, while it is 47.4422 dB in [22]. MITEGO method has improved PSNR value of Pepper image by 2.2518 dB, which is equal to 4.53 %. These results are presented in Fig. 15.

8. CONCLUSIONS In this research, we proposed a new robust multilayer image steganography method called MITEGO method. MITEGO method is designed to overcome the weaknesses of the previous related works in the field, and to provide more secure image steganography at different points. It follows a set of rules to create a stego image object (stego file) to embed and extract a secret message based on two layers: Hardening Layer and Embed/Extract Layer. Subsequently, MiTEGOsoft software tool is developed based on MITEGO method. MiTEGOsoft software tool is developed by using Java programming language to embed and extract a secret message to and from digital images. Finally, MITEGO method is evaluated based on experiments and analysis of corresponding results. The research calculated Peak Signal-to-Noise Ratio (PSNR) in decibel (dB) unit by using ImageMagick software tool for each generated stego file. The obtained results are compared to the previous related works to prove the significant of our designed MITEGO method and developed MiTEGOsoft software tool. As shown, this research contributed significant improvements by up to 15.46%, in the area of image steganography. Hopefully future works will expand MITEGO method to handle vector images, such as: svg images format, 3D images, and Monoscopic (360-degree) and Stereoscopic videos.

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