An Investigation into Encrypted Message
Hiding Through Images Using LSB
FAHIM IRFAN ALAM
Dept. of Math, Stat & Computer Science, St. Francis Xavier University. Antigonish, Nova Scotia, B2G 2W5, Canada.
FATEHA KHANAM BAPPEE
Dept. of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
FARID UDDIN AHMED KHONDKER
Dept of Computer Science and Engineering, University of Chittagong, Bangladesh.
ABSTRACT
Data security has become a cause of concern because of the electronic spying especially in the military, government and many sectors of communication fields. This paper investigates a representation for embedding secure data within an image based on substitution method which gives the scope of large amount of secret message hiding within digital images. Specifically we used least significant bit (LSB) substitution method to encrypt the message in image file. For improving the performance of LSB which is very simple by nature, we performed noise filtering at the beginning of the process to ensure noise-free data to be transmitted through the image. Also, after the extraction of the secure message at the receiver portion of the network, we used Automatic Repeat reQuest (ARQ) method as the error detection and correction process to ensure that the correct data has been transmitted and no information is lost. The improved framework resulted in satisfactory outcomes.
Keywords: Least Significant Bit (LSB), Automatic Repeat reQuest (ARQ), Substitution, Filtering.
1. INTRODUCTION
Security becomes increasingly important for many applications, such as confidential transmission, video surveillance, military and medical applications. Data hiding has been used for thousands of years to transmit data without being intercepted by unwanted viewers. In the world of data transmission there has always been a need to hide information. For example, the importance of fast and secure diagnosis is very critical in the medical world. Images can be used as the transmission of such secure data because the use of digital images has increased rapidly on the Internet. The transmission of images is taking place very frequently and images containing secure data have been also proved to be very useful in many application. Therefore, it has become very necessary to find an efficient way to transmit images carrying secure data over networks. One of the most widely used methods is encryption which takes data and mixes it up with an algorithm and a key to output unreadable data. The receiver then must also have a key to decrypt the data in order to read it. Various encryption or data hiding algorithms are used to ensure the protection of this multimedia data. For secure transmission of data, a problem is to try to combine encryption and data hiding in a single step.
Another form of data hiding is encryptions cousin steganography [11] which can be stated as the act of hiding data inside other data. In today’s society the most practical implementation of Steganography is used in the world of computers. Data is the heart of computer communication and over the year a lot of methods have been created to accomplish the goal of using Steganography to hide data. The trick is to embed the hidden object into a significantly larger object so the change is undetectable by the human eye. The best object is probably a digital image. Digital images have the benefit of containing massive amounts of bytes to designate pixel color for the photo. It is important to understand the process by which digital Steganography takes place, and to make sure the cover image is large enough to support the byte manipulation [12],[13].
encryption key in the receiver part with the key that is given at the sender portion. The correct matching will finally lead the receiver towards the extraction of message.
The objectives of this paper include the followings-
1. Collecting images from some reliable and consistent source. 2. Preprocessing of images for the sake of producing correct result.
3. Embedding of the secret text message in the digital images using an encryption key. 4. Transmitting the image containing secret data.
5. Accurate extraction of the secret data at the receiver.
The rest of the paper is organized as follows. Section 2 discusses the basic concepts related to data hiding with its uses, features and application. Existing works on secure data transmissions are reviewed on section 3. Steganography as a form of data hiding is explained in section 4 followed by a detail description Least Significant Bit (LSB) substitution method that we are going to use as our data encryption method in section 5. Section 6 provides the experimental results of the implementation of our proposed methodology and finally section 7 concludes the paper with an indication of the future research.
2. DATA HIDING
We can define data hiding [11] as a class of processes used to embed useful and secure data, for example, copyright information, into various forms of media such as text, audio, or image with least amount of perceivable degradation to the original data. It means that the embedded data should be in an invisible form to a human observer. Digital representation of media facilitates access and potentially improves the portability, efficiency, and accuracy of the information presented. Undesirable effects of simplistic data access include an increased opportunity for copyright violation and content tampering or modification.
2.1. Uses and Applications of Data Hiding
Two important uses of data hiding in digital media can be considered in terms of proof of the copyright and content integrity assurance. Therefore, the secure data should be kept hidden in the host signal, even if there is a possibility that the signal is subjected to manipulation as degrading as resampling, cropping, filtering, or lossy data compression.
Trade-offs exists between the quantity of embedded data and the degree of immunity to host signal modification. By constraining the degree of host signal degradation, a data-hiding method can operate with either high embedded data rate, or high resistance to modification, but not both. Because when one increases, subsequently the other decreases. In some data-hiding systems such as a spread spectrum, this can be shown mathematically which actually seems to hold true for other data-hiding systems. In any system, we can deal bandwidth for robustness by exploiting redundancy.
The quantity of embedded data and the degree of host signal modification vary from application to application. Different techniques have been employed for different applications. Several prospective applications of data hiding are discussed in this section.
An application that requires a minimal amount of embedded data is the placement of a digital water mark. The hidden secure data are used as an indication of ownership in the host signal, serving the same intention, for example, as an author’s signature or a company logo. The critical nature of the information results in the signal facing intelligent and intentional attempts to destroy it and thus the coding techniques [14] must be invulnerable to a wide variety of possible modifications.
Another application, feature location, requires more data to be embedded. In this application, the embedded data are hidden in specific locations within an image. It enables one to identify individual content features, e.g., the name of the person on the left versus the right side of an image. Typically, feature location data are not subject to intentional removal. However, it is expected that the host signal might be subjected to a certain degree of modification, e.g., images are routinely modified by scaling, cropping, and tone scale enhancement. As a result, feature location data hiding techniques must be immune to geometrical and non-geometrical modifications of a host signal.
Format (TIFF). These problems can be resolved by embedding those important annotations directly into the data structure of the host signal.
2.2. Challenges of Data Hiding
Data-hiding techniques should be capable of embedding data in a host signal with the following formidable restrictions and features which are the focuses of our problem.
(1) The host signal should be non-objectionably degraded and the embedded data should be minimally observable.
(2) The data to be securely transmitted, should be directly encoded into the image or other media, rather than encoded into a header, so that the data remain unharmed across varying data file formats.
(3) The embedded data should be protected to modifications ranging from intentional and intelligent attempts at removal to anticipated manipulations, e.g., channel noise, filtering, resampling, cropping, encoding, lossy compressing, printing and scanning, digital-to-analog (D/A) conversion, and analog-to-digital (A/D) conversion, etc.
(4) Asymmetrical coding of the embedded data is desirable, since the purpose of data hiding is to keep the data in the host signal, but not necessarily to make the data difficult to access.
(5) Error correction coding should be used to ensure data integrity. It is expected that there will be some degradation to the embedded data when the host signal is modified.
3. RELATED WORK
In[2], a new framework of data hiding is presented which aimed to hide information ( a data file) in any execution (EXE) file. The author also established a relationship between stenography and cryptography. The encryption method which has been employed in this paper is Advanced Encryption Standard (AES)method. In [1], the author presented an authentication protocol which serves as a proof of concept for validating an RFID tag to a reader device by using the AES as cryptographic primitive. The major contribution of this work is the AES hardware implementation that encrypts a 128-bit block of data in 1000 clock cycles. In [3], the author uses image filtering and adaptive image segmentation along with bits replacement on the right pixels. These pixels are not selected sequentially but rather selected randomly by using new concept defined by main cases for each bit in one pixel. There are many approaches that are called cover escrow schemes, where it is required to possess the original cover signal in order to get back the hidden information. These approaches are explained in [4], [5], and [6].
Neil F. Johnson and sushil jajodia [7] have given several characteristics in data hiding methods to identify the existence of a hidden message where the images are reviewed and used a steganographic tool which tests robustness of data hiding techniques in images such as cropping rotating, warping and blurring. Lisa M. Marvel and Charles T. Retter [8] presented an approach of embedding information within images, called Spread Spectrum Image Steganography (SSIS) which conceals a message of extensive length with in images but at the same time maintaining the original image size. Giuseppe Mastronardi et al., [9] have analyzed the effects of Steganography for different types of image formats (DWT, BMP, GIF, JPEG). Based on the analysis, he proposed two different approaches in form of creating an “adhoc” palette for BMP and GIF images. LUI Tong and QIU Zheng-ding [10] introduced a Quantization-based Steganography scheme which conceals the secret message in every chrominance component of a color. The Quantization-based hiding method is mostly free from the effect of interference and the experimental simulation results the hidden message to be extracted.
4 LEAST SIGNIFICANT BIT (LSB) SUBSTITUTION
Least Significant Bit (LSB) substitution method is a very popular way of embedding secret messages with simplicity. The fundamental idea here is to insert the secret message in the least significant bits of the images. This actually works because the human visual system is not sensitive enough to pick out changes in color whereas changes in luminance are much better picked out.
A basic algorithm for LSB substitution is to take the first N cover pixels where N is the total length of the secret
message that is to be embedded in bits. After that every pixel's last bit will be replaced by one of the message bits [15],[16]. As an example, suppose that we have three adjacent pixels (nine bytes) with the following RGB encoding:
Now suppose we want to "hide" the following 9 bits of data (the hidden data is usually compressed prior to being hidden): 101101101. If we overlay these 9 bits over the LSB of the 9 bytes above, we get the following
(where bits in bold have been changed):
10010101 00001100 11001001
10010111 00001110 11001011
10011111 00010000 11001011
We see that here we have successfully hidden 9 bits but only at the cost of changing 4, (roughly 50%), of the LSBs. This paper concerns with hiding text data containing secret message within images with the Least Significant Bit (LSB) substitution procedure. Therefore, a general and common model for evaluating the entire process is required. The procedure should include all the steps relevant with the desired message hiding within images. This process must begin with gathering images that are to be operated on. Further, the images have to produce relevant and key variables that will significantly describe the environment.
Two of the steps are the most important phases of Least Significant Bit (LSB) substitution procedure. They are embedding and extraction of secret text message. Because with these two processes, the main function of embedding the data into images and further extraction will be done. The entire procedure functions with the following four major steps:
(1) Loading host image
(2) Data Embedding
(3) Transmission
(4) Data Extraction.
Fig 1. Least Significant Bit (LSB) substitution procedure
After the host image is located, the embedding and extraction will be done and finally the receiver will be able to enjoy the original message.
5. PROPOSED FRAMEWORK
In order to approach the solution in a modular manner, our proposed system is divided into several modules. These modules are formed in a way so that the output of every module becomes the input for the next module. However, the primary input of the system is a text to be embedded. There are three main modules which are further divided into sub-modules as:
1. Pre-processing Module 2. Data Embedding Module 3. Data Extraction Module
4. Error Detection/Correction Module We will discuss the modules in detail below-
5.1. Preprocessing Module
Pre-processing or the input module consists of acquiring the image file and then performs the necessary calculations to estimate the pixel values of red, green and blue color in the image. This will be followed by performing filtering process which will remove any noisy information from the original data. Details about sub-module are specified as follows.
To embed data within an image file, the first task is to choose the relevant variables that effectively describe the environment. For that, it is necessary to identify the specific attributes and characteristics of the image in terms of specifying the pixel values of red, green and blue colors. The host image can be captured by using a digital camera and/or a conventional camera with a scanner.. Secret data that are to be hidden can be in any kind of digital form. The text containing the data to be hidden will be chosen by the sender. There is a specific type of image that is used. That is the host image i.e. in which the secret data is to be embedded. Moreover, a password will be given by the sender in order to keep the data safe and to avoid access from malicious users. Therefore, the three types of input parameters are:
1. A host image
2. A text containing the secret data 3. A password
5.1.1. Estimating RGB pixel values
RGB pixel values are calculated from the host image using the following algorithm:
Algorithm for Determining Pixel Values of Red, Green and Blue
For row = 0 To picwidth
For column = 0 To picheight pointRGB =Picture.Point(m, n) red(m, n) = pointRGB Mod 256
green(m, n) = ((pointRGB And &HFF00FF00) / 256&) blue(m, n) = (pointRGB And &HFF0000) / (256& * 256&)
Here, picwidth represent number of rows and picheight represent number of columns of the image [17].
5.1.2. Filtering
Information embedded in images suffers from loss of perfection if the images contain noises. Therefore, the procedure may not produce the true data. We have paid particular attention for unavoidability of these errors. In image processing it is really desirable to carry out a certain degree of noise reduction process in an image before performing other higher-level processing steps, such as encryption and decryption in order to ensure safe data transmission. In this paper, we have particularly used median filtering [ ] which is a non-linear strategy that replaces pixels with the median value in the neighborhood.
Algorithm for performing median filtering [18]: allocate output Pixel Value image_width image_height
edgeofy := window_height / 2 rounded_down
for x from edgeofx to image_width - edgeofx
for y from edgeofy to image_height - edgeofy
allocate color Array window_width window_height
for fx from 0 to window_width
for fy from 0 to window_height
colorArrayfxfy := input Pixel Valueofx + fx - edgeofxy + fy - edgeofy
Sort all entries in color Array
Output Pixel Valueofxy := color Array window_width / 2window_height / 2
5.2. Embedding Module
This module is one of the most important parts of the entire system. In this module, the basic operation involving embedding the secret message into the image file will be done. The process consists of the following sub modules:
1. Converting the input values into ASCII decimal value 2. Further converting the input values into binary 3. Converting the RGB values into binary 4. Perform substitution using encryption key
5.2.1. Calculating ASCII Values of Inputs
As stated earlier, the modules in this system are organized in such a way so that every output will act as input for the next module. Therefore, the password along with the secret message will act as input values for the embedding module. This module begins by calculating the ASCII decimal values of the inputs i.e., the password and the text containing secret message.
5.2.2. Converting into Binary
Next, we have to convert every character of password and encrypted message into eight bit binary stream (from ASCII decimal value). The procedure containing the calculation is given below-
string binary(int a) { i = 0;
do {
arr[i++] = a % 2; a /= 2;
} while (a != 0); string bin = "";
for (int j = i - 1; j >= 0; j--) bin += arr[j].ToString(); return bin;
5.2.3. Converting RGB into binary
The host image containing the secret data is further manipulated in terms of converting the RGB pixel values into binary. The procedure is the same as stated in the previous step.
5.2.4. Substitution
Fig 2. Pixel Splitting into Bit and Character Bit Insert
Character Insert into The RGB Octet
Add the letter "W" to a 24-bit image file:
W = 01010111 (ASCII)
R G B
R G B
[10000100 10110110 11100111] [10000100 10110111 11100110]
[10000101 10110111 11100111] 10000101 10110110 11100111]
[10000101 10110110 11100111] 10000101 10110111 11100111]
for (int i = bit.Width-1; i >= 0 && tag; i--) {
for (int j = bit.Height-1; j >= 0 && tag; j--) {
count++;
if (len!=0 && ( m == len)) tag = false;
else {
if (count == 21) {
len = bit.GetPixel(i, j).B; }
else if (count > 21) {
pass += Convert.ToString( Convert.ToChar(bit.GetPixel(i, j).B)); m++;
} } }
5.3.2. Perform the extraction
This step works by extracting the LSB of every binary stream upto the message length. The procedure performing the operation is as follows-
if (k < 15) {
len += r[r.Length - 1]; len += g[g.Length - 1]; len += b[b.Length - 1]; k += 3;
}
else if (k == 15) {
len += r[r.Length - 1];
str_len = calculate_decimal(len); str_len *= 8;
msg += g[g.Length - 1]; msg += b[b.Length - 1]; t += 2;
k++; } else {
msg += r[r.Length - 1]; msg += g[g.Length - 1]; msg += b[b.Length - 1]; t += 3;
} }
5.3.3. Separating the secret message
Fig. 3. Flowchart containing Embedding and Extraction Processes
5.4. Error Detection/Correction Module
For error detection and correction purpose, we have used Automatic Repeat reQuest (ARQ) method [19], [22] which is an error control mechanism for data transmission that makes use of error-detection codes, sending
acknowledgments and time lengths to achieve proper data transmission. An acknowledgment is the message that
is sent by the receiver to indicate that it has correctly received the data or information. Usually, when the sender does not receive the acknowledgment before the specified time, it resends the data until it is correctly received by the receiver. In this paper, we have implemented Selective Repeat ARQ. The concepts related to this are explained in [20],[23].
6. IMPLEMENTATION & RESULTS
For the implementation purposes, we used Microsoft Visual Studio .NET (C #) as the implementation tool for developing the design.
6.1. Working of Encoding and Decoding the Image
This section focuses on the implementation of the GetEncodedBitmap() and GetEncodedMessage() methods of the SteganographyWse web service class [21]. The GetEncodedBitmap() method accepts a bitmap identifier (the unique ID for the DIME attachment carrier image), a string password, and a string message as parameters, and returns another unique image identifier (the unique ID for the DIME attachment encoded image). This method uses the bitmap identifier to extract the carrier image from the DIME attachment collection of the
Start
Identify the LSB Of pixel
Hide the ASCII value of encrypted message character to the LSB of pixel.
Message character
is finished N
scan the message
End Change
technique
Next character
Third party Suspect the data Y
Y
N
RequestContext object into a MemoryStream . The MemoryStream is then passed, along with the password and the text to be hidden to the private EncodeBitmap() method (described later).
The GetEncodedMessage() method accepts a bitmap identifier (the unique ID for the DIME attachment encoded image) and a string password as parameters, and returns a string (which is the hidden text in the encoded image).The method uses the bitmap identifier to extract the encoded image from the DIME attachment collection of the RequestContext object into a memory stream. This memory stream is then passed (by reference), along with the password to the private DecodeBitmap() method (described later). The password is used to encrypt the hidden message during the process of inclusion into the carrier image. The same password is used during the decryption process to extract the hidden message from the carrier image. The median filtering procedure also is implemented in this method.
Fig. 4(a). Encoding Password Box
Fig. 4(b). Decoding Password Box
Now let's see the actual implementations of the EncodeBitmap() and DecodeBitmap() methods. These methods are responsible for the actual steganographic process. The EncodeBitmap() method takes a BitmapStream , a password, and the text to be hidden as parameters, and uses an algorithm (described later) to inject the data into the bitmap stream using the password specified as the encryption key. Similarly, the DecodeBitmap() method takes a BitmapStream and a password, and uses the same algorithm (in the reverse mode) to extract the message from the BitmapStream using the password as the decryption key. The functionality of these methods is pretty contradictory (one encodes, while the other decodes) yet similar, and the code for both of these methods is shown in the next section. They are self-explanatory, but relevant code comments about the encryption/decryption algorithms are included.
Sample Input
The first image in the form is the innocent raw image which is fresh and has no contain any secret data .then
write in the text box secret message .and pressing encode button inject the message into the image and message
containing image shown in picturebox2 .
Fig. 5(b). Encrypted Message
Sample Output
Fig. 6. Sample Output
We performed the proposed framework for three types of images (JPEG, BMP, GIF) and got the following results –
Table 1: Result Analysis for Different Types of Images
Image Type Number of Images No. of Successful Transmission Success Rate
JPEG 35 29 82.85%
BMP 35 31 88.57%
GIF 35 28 80.0%
For all the three types of images, the rate of successful transmission is equal to or above than 80% which is really satisfactory.
7. CONCLUSION AND FURTHER RESEARCH
With LSB Substitution it is easy to tell if an image has been Steganographed with an Enhanced LSB Attack. A complex image yields a much better Steganographed image—in that is harder to visually detect Steganography. We have successfully implemented the LSB method and got satisfactory results as we also took into account the filtering option for making sure that the image is noise-free before transmission. Also, the error detection and correction stage finally ensures the correct data has been transmitted.
An interesting part of our framework is this work pre-encrypts the data so that before a user applies a password, the message is already encrypted. This helps to render the frequency analysis useless because it does not just implement the alphabet but the entire ASCII table.
We hope to add support to hide all file formats. This allows for a much broader spectrum of uses: one would be able to encode .exe, .doc, .pdf, .mp3, and video file. The program would be more versatile because often hiding text just isn’t enough. We also would like to implement batch image processing and statistical analysis so that we can run the program through a dataset of images and detect data hiding and perhaps crawl through Google Image Search to see how prevalent data hiding is.
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