Abstract: The goal of steganography is to avoid drawing suspicion to the transmission of a hidden message. If suspicion is raised, steganography may fail. The success of steganography depends on the secrecy of the action. If steganography is detected, the system will fail but data security depends on the robustness of the applied algorithm. In this study, the secret message are compress and encrypt it by the receiver’s public key along with the stego key and embed both messages in a carrier using an embedding algorithm. The stego-image is the result we get by running the algorithm you select on the message (file to hide) and cover (image). It can be saved into BMP or PNG format. The reason that it can only be saved in these formats is because they are lossless, there is no information lost as part of the file formatting.

INTRODUCTION

Steganography refers to the science of invisible communication. Unlike cryptography where the goal is to secure communications from an eavesdropper, steganographic techniques strive to hide the very presence of the message itself from an observer. The general idea of hiding some information in digital content has a wider class of applications that go beyond steganography (Fig. 1). The techniques involved in such applications are collectively referred to as information hiding. For example, an image printed on a document could be annotated by metadata that could lead a user to its high resolution version.

In general, metadata provides additional information about an image. Although metadata can also be stored in the file header of a digital image, this approach has many limitations. Usually when a file is transformed to another format (e.g., from TIFF-JPEG or to BMP), the metadata is lost. Similarly, cropping or any other form of image manipulation destroys the metadata. Finally, metadata can only be attached to an image as long as the image exists in the digital form and is lost once the image is printed. Information hiding allows the metadata to travel with the image regardless of the file format and image state (digital or analog). A special case of information hiding is digital watermarking. Digital watermarking is the process of embedding information into digital multimedia content such that the information (the watermark) can later be extracted or detected for a variety of purposes including copy prevention and control (Karzenbeisser and Perircolas, 2000).

Fig. 1:	Relationship of steganography to related fields

Digital watermarking has become an active and important area of research and development and commercialization of watermarking techniques is being deemed essential to help address some of the challenges faced by the rapid proliferation of digital content (Furht and Kirovski, 2005). The key difference between information hiding and watermarking is the absence of an active adversary. In watermarking applications like copyright protection and authentication, there is an active adversary that would attempt to remove, invalidate or forge watermarks. In information hiding there is no such active adversary as there is no value associated with the act of removing the information hidden in the content. Nevertheless, information hiding techniques need to be robust against accidental distortions. Unlike information hiding and digital watermarking, the main goal of steganography is to communicate securely in a completely undetectable manner.

Fig. 2:	General model for steganography

Steganography provides a means of secret communication which cannot be removed without significantly altering the data in which it is embedded. The embedded data will be confidential unless an attacker can find a way to detect it as shown in Fig. 2.

TYPES OF STEGANOGRAPHY

Steganography can be split into two types, these are Fragile and Robust. The following section describes the definition of these two different types of steganography. Fragile steganography involves embedding information into a file which is destroyed if the file is modified. This method is unsuitable for recording the copyright holder of the file since it can be so easily removed but is useful in situations where it is important to prove that the file has not been tampered with such as using a file as evidence in a court of law, since any tampering would have removed the watermark. Fragile steganography techniques tend to be easier to implement than robust methods.

Robust marking aims to embed information into a file which cannot easily be destroyed (Cox et al., 2002). Although, no mark is truly indestructible, a system can be considered robust if the amount of changes required to remove the mark would render the file useless. Therefore, the mark should be hidden in a part of the file where its removal would be easily perceived.

There are two main types of robust marking. Fingerprinting involves hiding a unique identifier for the customer who originally acquired the file and therefore is allowed to use it (Karthik et al., 2008a). Should the file be found in the possession of somebody else, the copyright owner can use the fingerprint to identify which customer violated the license agreement by distributing a copy of the file. Unlike fingerprints, watermarks identify the copyright owner of the file, not the customer (Karthik et al., 2008b). Whereas, fingerprints are used to identify people who violate the license agreement watermarks help with prosecuting those who have an illegal copy. Ideally fingerprinting should be used but for mass production of CDs, DVDs, etc it is not feasible to give each disk a separate fingerprint.

Watermarks are typically hidden to prevent their detection and removal, they are said to be imperceptible watermarks. However, this need not always be the case. Visible watermarks can be used and often take the form of a visual pattern overlaid on an image. The use of visible watermarks is similar to the use of watermarks in non-digital formats (such as the watermark on British money).

ALGORITHMS USED IN STEGANOGRAPHY

There are four algorithms currently implemented, each use least significant bit steganography and some filter the image first.

BlindHide: This is the simplest way to hide information in an image. It blindly hides because it just starts at the top left corner of the image and works it's way across the image (then down-in scan lines) pixel by pixel. As it goes along it changes the least significant bits of the pixel colours to match the message (Karthik et al., 2009a). To decode the process the least significant bits starting at the top left are read off. This is not very secure it's really easy to read off the least significant bits. It also isn't very smart if the message doesn't completely fill up the possible space then just the top part of the image is degraded but the bottom is left unchanged making it easy to tell what's been changed.

HideSeek: This algorithm randomly distributes the message across the image. It is named after Hide and Seek a Windows 95 steganography tool that uses a similar technique. It uses a password to generate a random seed then uses this seed to pick the first position to hide in. It continues to randomly generate positions until it has finished hiding the message (Karthik et al., 2008c). It's a little bit smarter about how it hides because you have to try every combination of pixels in every order to try and crack the algorithm unless you have the password. It's still not the best method because it is not looking at the pixels it is hiding in it might be more useful to figure out areas of the image where it is better to hide in.

FilterFirst: This algorithm filters the image using one of the inbuilt filters and then hides in the highest filter values first. It is essentially a fancier version of BlindHide as it doesn't require a password to retrieve the message. Because we are changing the pixels we need to be careful about filtering the picture because we don't want to use information for filtering that might change. If we do, then it may be difficult (if not impossible) to retrieve the message again. So this algorithm filters the most significant bits and leaves the least significant bits to be changed. It is less noticeable on an image because using the filter ensures, hiding in the parts of the image that are the least noticeable.

BattleSteg: The best of all. This algorithm performs Battleship Steganography. It first filters the image then uses the highest filter values as ships. The algorithm then randomly shoots at the image (like in HideSeek) and when it finds a ship it clusters it's shots around that hit in the hope of sinking the ship. After a while it moves away to look for other ships. The effect this has is that the message is randomly hidden but often hidden in the best parts to hide in thanks to the ships. It moves away to look for other ships so that we don't degrade an area of an image too greatly. It is secure because you need a password to retrieve the message. It is fairly effective because it is hiding (if you set the values right) the majority of the information in the best areas.

Dynamic BattleSteg and FilterFirst: These two algorithms do the same as BattleSteg and FilterFirst except they use dynamic programming to make the hiding process faster and less memory intensive. They are not compatible with the original algorithms because the order of pixels kept in the dynamic array is not exactly the same.

IMAGE TECHNIQUES

Least significant bit: LSB-Least Significant Bit Hiding (Image Hiding). This method is probably the easiest way of hiding information in an image and yet it is surprisingly effective. It works by using the least significant bits of each pixel in one image to hide the most significant bits of another (Wu and Liu, 2003). So in a JPEG image for example, the following steps would need to be taken:

Hiding depends on the settings you choose but as an example if we hide in the 2 least significant bits then can hide: MaxBytes = (image.height() * image.width() * 3 * 2)/8; i.e., the number of pixels, times the number of colours (3), times the number of bits to hide in, all divided by 8 to get the number of bytes (Karthik et al., 2009b). It helps to hide a bit less than this because the algorithms may take a while to find places that haven't had anything hidden in it when some one close to the threshold.

TYPES OF FILTERS

There are 2 different filters the Laplace filter and the Sobel filter. Traditionally, these filters are used for detecting edges in pictures. As a side effect they happen to pick the best areas to change (Johnson and Jajodia, 1998). This is because an edge has a really light pixel next to a darker one. If the lighter pixel make darker and the darker pixel lighter we aren't going to notice as much as if make two pixels are the same color different. The Sobel filter is better at detecting edges but the Laplace filter is better at picking up noise.

The embedding rate bar shown on the encoding and simulation panels shows the percentage of space available for steganography that will be written to. Anything <10% is a good rate. You can decrease the embedding rate by using a smaller message, a larger image or by changing the number of bits that will be written to (in the algorithm options). For steganalysis, the embedding rate is an approximation of the percent of space available that has been written to. However, the steganalysis techniques only use the very least significant bit in each color for their calculation.

STEG ANALYSIS

For hiding information, an equal number of clever techniques have been designed to detect the hidden information. These techniques are collectively known as steganalysis. As introduced earlier, the Laplace formula is one such steganalytic method. Two other popular techniques are RS Analysis and Sample Pairs Analysis.

RS Analysis makes small modifications to the least significant bit plane in an image then uses these modifications and a discrimination function to classify groups of pixels (Lu, 2004). The counts of the groups based on the modifications allow the calculation of an estimated embedding rate. Images that do not contain steganography often have a natural embedding rate of up to 3%, whereas images containing hidden information usually have estimated embedding rates which accurately reflects the amount of hidden information.

RS Analysis is a special case of Sample Pairs Analysis which also uses least significant bit modifications to help calculate an estimated embedding rate. Sample pairs analysis utilises finite state machines to classify groups of pixels modified by a given pattern. Both steganalysis techniques are very accurate at predicting the embedding rate on stego-images using least significant bit embedding. Since the two proposed techniques, FilterFirst and BattleSteg, both use least significant bit embedding, RS Analysis and Sample Pairs Analysis can use to compare them against more traditional techniques such as BlindHide and HideSeek.

NEED FOR DATA HIDING

PLAUSIBLE DENIABILITY

Plausible deniability is defined as encryption scheme is deniable if the sender can generate plausible keys and random choices that will satisfy the authority and at the same time keep the past communication private.

In this study, a novel plausible deniability scheme in steganography was proposed by using a diversionary message and encrypt it with a DES-based algorithm (Huang et al., 2008). Then, the secret message is compressed and encrypt it by the receiver’s public key along with the stego key and embed both messages in a carrier using an embedding algorithm (Fig. 3).

Fig. 3:	Graph of Embedding Rate versus Machine Learning Accuracy

It will be demonstrated how this method can support plausible deniability and is robust against steganalysis? For implementation of the proposed method, the following five steps are considered:

Step 1: Construct cipher text as cover medium, Let m be the plain text, E encription algorithm, K encription key and m_c cipher text then:

Step 2: Embed secret message in the cipher text, Let SE be the steganography algorithm, K_s the stago key, M the secret message, SG the stego object then:

Step 3: Uncover secret meesage from stego object, Let SD be the algorithm for recovering the secret message using the same stego key then:

Step 4: Deny secret communications reveal encription key; K to uncover the cover medium plain text to deny information hiding thus:

Step 5: Verificetiom encription of the resulting text in step 4 must give the stego object that is:

The condition for plausible deniability is:

CONCLUSION

Success in steganographic secrecy results from selecting the proper mechanisms. However, a stego medium which seems innocent enough may upon further investigation, actually broadcast the existence of embedded information. Development in the area of covert communications and steganography will continue. Research in building more robust methods that can survive image manipulation and attacks continues to grow. The more information is placed in the public's reach on the Internet, the more owners of such information need to protect themselves from theft and false representation. Systems to recover seemingly destroyed information and steganalysis techniques will be useful to law enforcement authorities in computer forensics and digital traffic analysis. The future enhancements can be done for location based hiding, more number of filters can be added and can use same stego image with different filters.

ACKNOWLEDGEMENTS

The researchers would like to thank the Director cum Secretary, Correspondent, Principal, SNS College of Technology, Coimbatore for their motivation and constant encouragement. The researchers would like to thank the Faculty Members of Department of Computer Science and Engineering for critical review of this manuscript and for his valuable input and fruitful discussions. Also, he takes privilege in extending gratitude to his family members and friends who rendered their support throughout this research work.