Abbas Cheddad

Review: Digital image steganography: Survey and analysis of current methods

Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganographys ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the object-oriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed.

A skin tone detection algorithm for an adaptive approach to steganography

Challenges face biometrics researchers and particularly those who are dealing with skin tone detection include choosing a colour space, generating the skin model and processing the obtained regions to fit applications. The majority of existing methods have in common the de-correlation of luminance from the considered colour channels. Luminance is underestimated since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful in the segregation of skin and non-skin clusters. To this end, here we use a new colour space which contains error signals derived from differentiating the grayscale map and the non-red encoded grayscale version. The advantages of the approach are the reduction of space dimensionality from 3D, RGB, to 1D space advocating its unfussiness and the construction of a rapid classifier necessary for real time applications. The proposed method generates a 1D space map without prior knowledge of the host image. A comprehensive experimental test was conducted and initial results are presented. This paper also discusses an application of the method to image steganography where it is used to orient the embedding process since skin information is deemed to be psycho-visually redundant.

A dynamic threshold approach for skin segmentation in color images

This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in color images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to segment human skin. These fixed thresholds mostly failed in two situations as they only search for a certain skin color range: 1) any non-skin object may be classified as skin if non-skin objectss color values belong to fixed threshold range. 2) any true skin may be mistakenly classified as non-skin if that skin color values do not belong to fixed threshold range. Therefore in this paper, instead of predefined fixed thresholds, novel online learned dynamic thresholds are used to overcome the above drawbacks. The experimental results show that our method is robust in overcoming these drawbacks.

Risk Factors and Tumor Characteristics of Interval Cancers by Mammographic Density

PURPOSE To compare tumor characteristics and risk factors of interval breast cancers and screen-detected breast cancers, taking mammographic density into account. PATIENTS AND METHODS Women diagnosed with invasive breast cancer from 2001 to 2008 in Stockholm, Sweden, with data on tumor characteristics (n = 4,091), risk factors, and mammographic density (n = 1,957) were included. Logistic regression was used to compare interval breast cancers with screen-detected breast cancers, overall and by highest and lowest quartiles of percent mammographic density. RESULTS Compared with screen-detected breast cancers, interval breast cancers in nondense breasts (≤ 20% mammographic density) were significantly more likely to exhibit lymph node involvement (odds ratio [OR], 3.55; 95% CI, 1.74 to 7.13) and to be estrogen receptor negative (OR, 4.05; 95% CI, 2.24 to 7.25), human epidermal growth factor receptor 2 positive (OR, 5.17; 95% CI, 1.64 to 17.01), progesterone receptor negative (OR, 2.63; 95% CI, 1.58 to 4.38), and triple negative (OR, 5.33; 95% CI, 1.21 to 22.46). In contrast, interval breast cancers in dense breasts (> 40.9% mammographic density) were less aggressive than interval breast cancers in nondense breasts (overall difference, P = .008) and were phenotypically more similar to screen-detected breast cancers. Risk factors differentially associated with interval breast cancer relative to screen-detected breast cancer after adjusting for age and mammographic density were family history of breast cancer (OR, 1.32; 95% CI, 1.02 to 1.70), current use of hormone replacement therapy (HRT; OR, 1.84; 95% CI, 1.38 to 2.44), and body mass index more than 25 kg/m(2) (OR, 0.49; 95% CI, 0.29 to 0.82). CONCLUSION Interval breast cancers in women with low mammographic density have the most aggressive phenotype. The effect of HRT on interval breast cancer risk is not fully explained by mammographic density. Family history is associated with interval breast cancers, possibly indicating disparate genetic background of screen-detected breast cancers and interval breast cancers.

An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution.

Optical projection tomography (OPT) imaging is a powerful tool for three-dimensional imaging of gene and protein distribution patterns in biomedical specimens. We have previously demonstrated the possibility, by this technique, to extract information of the spatial and quantitative distribution of the islets of Langerhans in the intact mouse pancreas. In order to further increase the sensitivity of OPT imaging for this type of assessment, we have developed a protocol implementing a computational statistical approach: contrast limited adaptive histogram equalization (CLAHE). We demonstrate that this protocol significantly increases the sensitivity of OPT imaging for islet detection, helps preserve islet morphology and diminish subjectivity in thresholding for tomographic reconstruction. When applied to studies of the pancreas from healthy C57BL/6 mice, our data reveal that, at least in this strain, the pancreas harbors substantially more islets than has previously been reported. Further, we provide evidence that the gastric, duodenal and splenic lobes of the pancreas display dramatic differences in total and relative islet and β-cell mass distribution. This includes a 75% higher islet density in the gastric lobe as compared to the splenic lobe and a higher relative volume of insulin producing cells in the duodenal lobe as compared to the other lobes. Altogether, our data show that CLAHE substantially improves OPT based assessments of the islets of Langerhans and that lobular origin must be taken into careful consideration in quantitative and spatial assessments of the pancreas.

Exploiting Voronoi diagram properties in face segmentation and feature extraction

Segmentation of human faces from still images is a research field of rapidly increasing interest. Although the field encounters several challenges, this paper seeks to present a novel face segmentation and facial feature extraction algorithm for gray intensity images (each containing a single face object). Face location and extraction must first be performed to obtain the approximate, if not exact, representation of a given face in an image. The proposed approach is based on the Voronoi diagram (VD), a well-known technique in computational geometry, which generates clusters of intensity values using information from the vertices of the external boundary of Delaunay triangulation (DT). In this way, it is possible to produce segmented image regions. A greedy search algorithm looks for a particular face candidate by focusing its action in elliptical-like regions. VD is presently employed in many fields, but researchers primarily focus on its use in skeletonization and for generating Euclidean distances; this work exploits the triangulations (i.e., Delaunay) generated by the VD for use in this field. A distance transformation is applied to segment face features. We used the BioID face database to test our algorithm. We obtained promising results: 95.14% of faces were correctly segmented; 90.2% of eyes were detected and a 98.03% detection rate was obtained for mouth and nose.

Image Processing Assisted Algorithms for Optical Projection Tomography

Since it was first presented in 2002, optical projection tomography (OPT) has emerged as a powerful tool for the study of biomedical specimen on the mm to cm scale. In this paper, we present computational tools to further improve OPT image acquisition and tomographic reconstruction. More specifically, these methods provide: semi-automatic and precise positioning of a sample at the axis of rotation and a fast and robust algorithm for determination of postalignment values throughout the specimen as compared to existing methods. These tools are easily integrated for use with current commercial OPT scanners and should also be possible to implement in “home made” or experimental setups for OPT imaging. They generally contribute to increase acquisition speed and quality of OPT data and thereby significantly simplify and improve a number of three-dimensional and quantitative OPT based assessments.

Enhancing Steganography in Digital Images

The history of steganography can be traced back to ancient civilization - the Persian and Greek conflict around 480 B.C and ancient Egyptian civilization - when steganography was first reported to exist. Steganography is the process of hiding information in a multimedia carrier. Steganalysis, which is the official counter attack science, has defeated Steganographic algorithms whether they are based on the traditional spatial domain or the transform domain. This paper discusses the possibility of embedding data in the frames of video files. We call it adaptive as we select the specific region of interest (ROI) in the cover image where we can safely embed our data. We chose these regions based on human skin tone colour detection. As such the method is obviously constrained to image or video files with face instances present.

A new colour space for skin tone detection

The majority of existing methods have one thing in common which is the de-correlation of luminance from the considered colour channels. It is believed that the luminance is underestimated here since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful in separating skin and non-skin clusters. To this end, this work uses a new colour space which contains error signals derived from differentiating the grayscale map and the non-encoded-red grayscale version. The advantages of this approach are the reduction of space dimensionality from 3D to 1D space and the construction of a rapid classifier necessary for real time applications. This method is meant to assist digital image steganography to orient the embedding process since skin information is deemed to be psycho-visually redundant.

Securing information content using new encryption method and Steganography

This paper proposes a novel encryption method with password protection based on an extended version of SHA-1 (secure hash algorithm) that is able to encrypt 2D bulk data such as images. There has been a modest research in the literature on encryption of digital images though. The algorithm benefits also from the conjugate symmetry exhibited in what is termed, herein, an irreversible fast Fourier transform (IrFFT). The proposed encryption method is a preprocessing phase which aims at increasing the robustness of image steganography against hackers. This scenario lays down a multi layer of security which forms a strong shield, against eavesdroppers, that is impossible to break. Both Shannon law requirements are met and results show promising results.