In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being
implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile
devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to
properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational
complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile
environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today
are capable of processing a majority of the available classification algorithms without concern of processing while the
same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system
targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance
shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The
methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.
There are over 250 image steganography methods available on the Internet. In digital image steganalysis an analyst has
three goals, first determine if an embedded message exists, next determine the embedding method used to create the
stego image and finally extract the hidden message. The objective of this paper lies on the second goal, that is, to
identify the embedding technique used to create the steganography image. Several detection systems currently exist, so
the identification problem becomes one of determining which detection system has correctly identified the embedding
method. In this work, the individual detection systems are fused using boosting. Boosting is a powerful technique for
combining an ensemble of base classifiers to produce a form of committee with improved performance over any of the
single classifiers in the ensemble. The results in this paper show that boosting takes advantage of the individual strengths
from each detection systems and classification performance is increased by 10%.
There are several security issues tied to multimedia when implementing the various applications in the cellular phone
and wireless industry. One primary concern is the potential ease of implementing a steganography system. Traditionally,
the only mechanism to embed information into a media file has been with a desktop computer. However, as the cellular
phone and wireless industry matures, it becomes much simpler for the same techniques to be performed using a cell
phone. In this paper, two methods are compared that classify cell phone images as either an anomaly or clean, where a
clean image is one in which no alterations have been made and an anomalous image is one in which information has
been hidden within the image. An image in which information has been hidden is known as a stego image. The main
concern in detecting steganographic content with machine learning using cell phone images is in training specific
embedding procedures to determine if the method has been used to generate a stego image. This leads to a possible flaw
in the system when the learned model of stego is faced with a new stego method which doesn't match the existing
model. The proposed solution to this problem is to develop systems that detect steganography as anomalies, making the
embedding method irrelevant in detection. Two applicable classification methods for solving the anomaly detection of
steganographic content problem are single class support vector machines (SVM) and Parzen-window. Empirical
comparison of the two approaches shows that Parzen-window outperforms the single class SVM most likely due to the fact that Parzen-window generalizes less.
Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are
over 250 different tools which embed data into an image without causing noticeable changes to the image. From a
forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool
that can scan and accurately identify files suspected of containing malicious information. The identification process is
termed the steganalysis problem which focuses on both blind identification, in which only normal images are available
for training, and multi-class identification, in which both the clean and stego images at several embedding rates are
available for training. In this paper an investigation of a clustering and classification technique (Expectation
Maximization with mixture models) is used to determine if a digital image contains hidden information. The steganalysis
problem is for both anomaly detection and multi-class detection. The various clusters represent clean images and stego
images with between 1% and 10% embedding percentage. Based on the results it is concluded that the EM classification
technique is highly suitable for both blind detection and the multi-class problem.
This article addresses four basic goals; 1) the evaluation of sequentially and randomly embedded stego evidence within digital images 2) the identification of "steganographic fingerprint" for special domain based steganographic methods, 3) the reduction of steganalysis false detection rate, and 4) the investigation of two well known pixel comparison based steganalysis methods. We present an improved version of Stego Sensitivity Measure, which is based on the statistics of sample pair (the basic unit), rather than individual samples which is very sensitive to ± A embedding. The presented measure enhances stego detection accuracy and localization of stego areas within sequentially and randomly embedded color or gray scale stego images. In addition, it estimates the message length of an embedded bit-stream within bit planes of a digital image, and it has better localization of steganography detected along with an improved estimation of the message length. It also identifies the "steganographic fingerprint" of special domain sequentially and randomly based steganographic methods. Numerical experimentation was conducted with an arbitrary image database of 200 color TIFF and RAW images taken with the Nikon D100 and the Canon EOS Digital Rebel cameras. In this article comparison are also shown using two known steganalysis methods Raw Quick Pairs and RS Steganalysis which have revealed that; a) The false alarm rate for the proposed detection method is p = 0.9 for a database of 200 images clean images while RS Steganalysis has shown a high false alarm rate for clean images of p = 2.8. b) The two methods Raw Quick Pairs and RS Steganalysis cannot be used for localization of steganographic regions due to the statistical properties of the detection methods.
Steganalysis has many challenges; which include the accurate and efficient detection of hidden content within digital images. This paper focuses on the development of a new multi pixel comparison method used for the detection of steganographic content within digital images transmitted over mobile channels. The sensitivity of detecting hidden information within a digital image can be increased or decreased to determine if slight changes have been made to the digital image for the target of blind steganalysis. The key thought of the presented method is to increase the sensitivity of features when alterations are made within the bit planes of a digital image. The differences between the new method and existing pixel comparison methods are; multiple masks of different sizes are used to increase the sensitivity and weighted features are used to improve the classification of the feature sets. Weights are also used with the various pixel comparisons to ensure proper sensitivity when detecting small changes. The article also investigates the reliability of detection and estimation length of hidden data within wireless digital images with potential for military applications emphasizing on defense and security.
There are several steganographic methods that embed in palette-based images. In general these schemes are using RGB palette models. The restrictions of palette-based image formats impose limitations on existing models. For example, how to divide colors from a palette-vector for embedding purposes without causing visual degradation to the image. Another crucial intricacy is embedding using multiple bit planes while preserving the image's characteristics. Possible solutions to these problems could be: a) using a multi-bit embedding procedure; b) using other color models and c) embedding only in non-informative regions. Therefore we present a new secure high capacity palette based steganographic method used to embed in multiple bit planes using different color models. The performance of the developed algorithm posts the following advantages shown through computer simulations: 1) Fewer modifications are present when compared to BPCS Steganographic method for palette-based images . 2) Provides additional security through a simple selective color and cover image algorithm. 3) The proposed method offers an increased capacity by embedding in multiple bit planes. 4) Finally, the secure media storage system contains an independent steganographic method that provides an additional level of security. The proposed method was proven to be immune to Chi-square and Pairs Analysis steganalysis attacks. In addition, the presented method uses different color model to represent the palettes. Analysis shows that the presented algorithm was also secure against detection from RS Steganalysis when using different color models.
The goal of this article is to investigate an alternative capacity for steganographic systems. We will define steganographic capacity as the maximum number of embeddable bit within a digital signal while maintaining imperceptible requirements. This capacity makes it somewhat possible to solve two fundamental steganographic problems: first, how to choose the best cover image among classes of images and second which embedding method may be employed to reduce the detection of hidden information within the embedded areas. In addition, the new capacity may be used for 1) the separation of an analyzed image into embeddable areas, 2) the identification of maximum embedding capacities within a cover digital image, and 3) estimating the length in bits used for embedding information within the identified regions.
This article presents a new approach, which focuses on the following problems: detection and localization of stego
informative regions within digital clean and noisy images; removing hidden data along with minimizing the statistical
differences between stego images and stego information removed image. The new approach is based on a new pixel
comparison and a new complexity measure. This new measure identifies the informative and stego-like regions of an
image, with the objective of stegoanalysis through the saving of informative regions and the discarding of stego-like
areas. The areas that are harder for detection are scanned in an alternate method in an attempt to detect areas that are
classified as good for embedding. This allows for a higher detection rate and a low false positive. Experimental results
will be presented in the complete write up. The data gathered will be listed on tables from a set of 100+ digital images.
The images used in the analysis will vary in size, format, and color. Various commonly employed (e.g., S-Tools,
SecurEngine, and wbStego3.51) approaches were used to hide hidden data onto the digital images for analysis. The new
method has shown remarkable detection accuracy and localization of embedded information for LSB embedding. We
have also shown that the presented method works even in the presence of noise in the image. In addition, this method
shows that an image can be divided into ideal detection areas and ideal embedding areas. With this in mind the image
can be scanned for the ideal detection methods to reduce both false positives and false negatives. This technique can be
applied to data compression and for hiding secret information, in both time and transformed domains. It is also
independent of the order color vectors in the palette.