Information Hiding Methods In An Image Computer Science Essay

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Image Steganalysis is process of detecting steganography in an image.It is an art of discovering hidden information and turning such secret information into useless data.Steganography has stood up as a challenge in cloud computing environment.Newer steganographic and watermarking techniques are devised and are worked on along with developement of better steganalysis tools and methods.

In this paper,we provide an overview of some characteristics of information hiding methods in an image and tools to find out existence of a hidden message and identify where to look for hidden information which are equally useful in cloud computing.The intent of this paper is to describe some methods of detecting and destroying hidden messages within computer images and understanding urgent need of using good steganalysis techniques in cloud computing environment.


By Stegnalysis suspected information streams are identified and if hidden messages are present into them, hidden information is recovered,if it is possible.The hidden message may be plaintext,

ciphertext, or anything that can be represented as a bit stream.For example, sending a satellite photograph hidden in another image.

Hiding information in an image requires alterations of certain properties of image which may ruin image or introduce some form of deprivation.At times,such changes may be visible to the human eyes, failing cause of steganography.These

characteristics may act as signatures that broadcast the

existence of the embedded message, thus defeating the

purpose of steganography.

Acording to the US National Institute of Standards and Technology (NIST),

“cloud computing is a model for

enabling convenient, on-demand

network access to a shared pool

of configurable computing resources.”

Cloud computing is location independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand.This unique attribute, however, poses many

new security challenges which have not been well understood.As among other resources and data large number of images migrate in cloud system,which are potential carriers of hidden information.

The rest of the paper is organized as follows:

Section 2 discusses ways/tools of hidding information in an image.

Section 3 briefly introduces difficulties of image stagnalysis.

Section 4 tools and methods for image stagnalysis with example.

Section 5 The paper concludes with Comments on image steganography, image steganalysis and related work.

Section 6 References.


Images are frequently transferred over internet to masses,and thus provide excellent medium for hidden transfer of information.There are many different methods of hiding information in

images,such as Least Significant

Bit (LSB) or noise insertions, manipulation of image and

compression algorithms, modification of image

properties such as luminance,transform domain technique.

The LSBs and transforms can be applied to hide

information in an image with virtually no impact to

the human sensory system.Unused space in file headers of image

can be used to hold “extra” information without degrading carrier.

Hiding information may require a stegokey,for example,

when a secret message is hidden within a cover image, the resulting product is a stego-image. A possible formula of the process may be represented as:

cover medium + embedded message + stegokey = stego-medium

A subset of steganography and digital watermarking tools to test detection properties and robustness to manipulations in efforts to destroy or disable the embedded message can be categorized into two groups: those in the Image Domain and those in the Transform Domain.

Image Domain tools encompass bit-wise methods that apply least significant bit (LSB) insertion and noise manipulation. These approaches are common to steganography and are characterized as "simple systems". The tools used in this group include StegoDos [Anon], S-Tools [Bro94], Mandelsteg [Has], EzStego [Mac], Hide and Seek (versions 4.1 through 1.0 for Windows 95) [Mar], Hide4PGP [Rep], Jpeg-Jsteg [Uph], White Noise Storm [Ara94], and Steganos [Hans]. The image formats typically used in such steganography methods are lossless and the data can be directly manipulated and recovered. Including additional components such as masks or image objects to watermark an image is an image domain approach that is somewhat independent of image format.

The transform domain grouping of tools include those that involve manipulation of algorithms and image transforms such as discrete cosine transformation (DCT) and wavelet transformation. These methods hide messages in more significant areas of the cover and may manipulate image properties such as luminance.Many transform domain methods are independent to image format and may survive conversion between lossless and lossly formats.

Some techniques share characteristics of both image and transform domain tools. These may employ patchwork, pattern block encoding, spread spectrum method and masking which add redundancy to the hidden information. These approaches may help protect against some image processing such as cropping and rotating. The patchwork approach uses a pseudo-random technique to select multiple areas (or patches) of an image for marking. Each patch may contain the watermark, so if one is destroyed or cropped, the others may survive. Masks may fall under the image domain as being an added component or image object. However, a mask may be added to an image by adjusting image properties or transform thus adopting characteristics of transform domain tools.


Steganography tools and methods insert information and manipulate the images in ways as to remain invisible to the human eye. However, any manipulation to the image introduces some amount of distortion and degradation of some aspect in the "original" image's properties. The tools vary in their approaches for hiding information. Without knowing which tool is used and which, if any, stegokey is used, detecting the hidden information may become quite complex. The challenge of steganalysis is that,the suspect images or information stream,may or may not have hidden data encoded into them.Hidden information may have been in some encrypted format.Suspect may have useless data encoded into it.Uncertainty is a big issue,making steganalysis a very expensive process in terms of time and effort.


The disabling or removal of hidden information in

images comes down to image processing techniques. For

LSB methods of inserting data, simply using a lossy

compression technique, such as JPEG, is enough to render

the embedded message useless.Images compressed with

such a method are still pleasing to the human eye but no

longer contain the hidden information.Tools exist to test the robustness of information hiding

techniques in images. These tools automate imageprocessing

techniques such as warping, cropping, rotating,and blurring.

To begin evaluating images for additional, hidden information, the concept of defining a "normal" or average image was deemed desirable. Defining a normal image is somewhat difficult when considering the possibilities of digital photographs, paintings, drawings, and graphics. Only after evaluating many original images and stego-images as to color composition, luminance, and pixel relationship do anomalies point to characteristics that are not "normal" in other images. Several patterns became visible when evaluating many images used for applying steganography. The chosen message and known cover attacks were quite useful in detecting these patterns. In images that have color palettes or indexes, colors are typically ordered from the most used colors to the least used colors to reduce table lookup time. The changes between color values may change gradually but rarely, if ever, in one bit shifts. Gray-scale image color indexes do shift in 1-bit increments, but all the RGB values are the same. Applying a similar approach to monochromatic images other than gray-scale, normally two of the RGB values are the same with the third generally being a much stronger saturation of color. Some images such as hand drawings, fractals and clip art may shift greatly in the color values of adjacent pixels. However, having occurrences of single pixels outstanding may point to the existence of hidden information.

One method for detecting the existence of hidden messages in stego-images is to look for obvious and repetitive patterns which may point to the identification or signature of a steganography tool or hidden message. Distortions or patterns visible to the human eye are the easiest to detect. An approach used to identify such patterns is to compare the original cover-images with the stego-images and note visible differences (known-cover attack). Minute changes are readily noticeable when comparing the cover and stego-images.Another visual clue to the presence of hidden information is padding or cropping of an image. With some stego tools if an image does not fit into a fixed size it is cropped or padded with black spaces. There may also be a difference in the file size between the stego-image and the cover image. Another indicator is a large increase or decrease in the number of unique colors, or colors in a palette which increase incrementally rather than randomly (except gray scale images).

There are several available steganographic detection tools such as EnCase by Guidance Software Inc., ILook Investigator by Electronic Crimes Program, Washington DC, various MD5 hashing utilities, etc. Stegdetect, provided by Niels Provos, is a program that detects data hidden in JPEG images using certain steganography-based applications.

Example of a Stegdetect output:

$ stegdetect *.jpg

dscf0001.jpg : outguess(old)(***) jphide(*)

dscf0002.jpg : negative

dscf0003.jpg : jsteg(***)

wonder-5.jpg : jphide(**)


Certain types of images are more likely to show up as false positives, such as, drawings, paintings, and images with monotone backgrounds.


Digital image steganography and its derivatives are growing in use and application.This paper provided an overview of image steganalysis,difficulties faced during steganalysis and introduced some methods of steganography in an image.

This work is but a very small fraction of the steganalysis approach. Methods of message detection and understanding the thresholds of

current technology are under investigation.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.This threat multiplies in cloud computing environment.

Systems to recover seemingly destroyed information and steganalysis techniques will be useful to law enforcement authorities in computer forensics and digital traffic analysis.