HUBFIRE - A multi-class SVM based JPEG steganalysis using HBCL statistics and FR Index

Blind Steganalysis attempts to detect steganographic data without prior knowledge of either the embedding algorithm or the 'cover' image. This paper proposes new features for JPEG blind steganalysis using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index); the Huffman Bit File Index Resolution (HUBFIRE) algorithm proposed uses these functionals to build the classifier using a multi-class Support Vector Machine (SVM). JPEG images spanning a wide range of resolutions are used to create a 'stego-image' database employing three embedding schemes - the advanced Least Significant Bit encoding technique, that embeds in the spatial domain, a transform-domain embedding scheme: JPEG Hide-and-Seek and Model Based Steganography which employs an adaptive embedding technique. This work employs a multi-class SVM over the proposed 'HUBFIRE' algorithm for statistical steganalysis, which is not yet explored by steganalysts. Experiments conducted prove the model's accuracy over a wide range of payloads and embedding schemes

Genotoxic effect induced by hydrogen peroxide in human hepatoma cells using comet assay

Background: Hydrogen peroxide is a common reactive oxygen intermediate generated by variousforms of oxidative stress. Aims: The aim of this study was to investigate the DNA damage capacity ofH2O2 in HepG2 cells. Methods: Cells were treated with H2O2 at concentrations of 25 μM or 50 μM for5 min, 30 min, 40 min, 1 h or 24 h in parallel. The extent of DNA damage was assessed by the cometassay. Results: Compared to the control, DNA damage by 25 μM and 50 μM H2O2 increasedsignificantly with increasing incubation time up to 1 h, but it was not increased at 24 h. Conclusions:Our Findings confirm that H2O2 is a typical DNA damage inducing agent and thus is a good modelsystem to study the effects of oxidative stress. DNA damage in HepG2 cells increased significantlywith H2O2 concentration and time of incubation but later decreased likely due to DNA repairmechanisms and antioxidant enzyme

Search for jet extinction in the inclusive jet-pT spectrum from proton-proton collisions at s=8 TeV

Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published articles title, journal citation, and DOI.The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7  fb−1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To test this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale

Searches for electroweak neutralino and chargino production in channels with Higgs, Z, and W bosons in pp collisions at 8 TeV

Searches for supersymmetry (SUSY) are presented based on the electroweak pair production of neutralinos and charginos, leading to decay channels with Higgs, Z, and W bosons and undetected lightest SUSY particles (LSPs). The data sample corresponds to an integrated luminosity of about 19.5 fb(-1) of proton-proton collisions at a center-of-mass energy of 8 TeV collected in 2012 with the CMS detector at the LHC. The main emphasis is neutralino pair production in which each neutralino decays either to a Higgs boson (h) and an LSP or to a Z boson and an LSP, leading to hh, hZ, and ZZ states with missing transverse energy (E-T(miss)). A second aspect is chargino-neutralino pair production, leading to hW states with E-T(miss). The decays of a Higgs boson to a bottom-quark pair, to a photon pair, and to final states with leptons are considered in conjunction with hadronic and leptonic decay modes of the Z and W bosons. No evidence is found for supersymmetric particles, and 95% confidence level upper limits are evaluated for the respective pair production cross sections and for neutralino and chargino mass values

SURF: Steganalysis using random forests

The success of any statistical steganalysis algorithm depends on the choice of features extracted and the classifier employed. This paper proposes steganalysis using random forests (SURF) employing HCS (Huffman Code Statistics) features and FR Index (ratio of File size to Resolution). The proposed algorithm is validated over an image database of over 30,000 images spanning various sizes, resolutions, qualities and textures to detect four widely used steganographic schemes namely LSB (Least Significant Bit) encoding, JPHS (JPEG Hide & Seek), MBS (Model Based Steganography) and PQ (Perturbed Quantization). The SURF algorithm proves random forest to be an efficient classifier for steganalysis and its performance is found to be superior compared to current steganalysis methods. © 2010 IEEE

JHUF-5 steganalyzer: Huffman based steganalytic features for reliable detection of YASS in JPEG images

Yet Another Steganographic Scheme (YASS) is one of the recent steganographic schemes that embeds data at randomized locations in a JPEG image, to avert blind steganalysis. In this paper we present JHUF-5, a statistical steganalyzer wherein J stands for JPEG, HU represents Huffman based statistics, F denotes FR Index (ratio of file size to resolution) and 5 - the number of features used as predictors for classification. The contribution of this paper is twofold; first the ability of the proposed blind steganalyzer to detect YASS reliably with a consistent performance for several settings. Second, the algorithm is based on only five uncalibrated features for efficient prediction as against other techniques, some of which employs several hundreds of predictors. The detection accuracy of the proposed method is found to be superior to existing blind steganalysis techniques. © 2010 Springer-Verlag

JPEG steganalysis using HBCL statistics and FR index

This paper introduces a new statistical model for blind steganalysis of JPEG images. The functionals used to build this model are based on Huffman Bit Code Lengths (HBCL statistics) and the file size to image resolution ratio (FR Index). JPEG images spanning a wide range of resolutions were used to create a 'stego-image' database employing three embedding schemes - the advanced Least Significant Bit encoding technique, JPEG Hide-and-Seek and Model Based Steganography. Existing blind steganalysis techniques mostly involve the analyses of generalized category attacks and the higher order statistics. This work builds an effective neural network prediction model using HBCL statistics and FR Index, which are not yet explored by steganalysts. The experimental results proved to be efficient over a diverse image database and several payloads. © 2010 Springer-Verlag

Classification of email using BeaKS: Behavior and keyword stemming

Spam mails are one of the greatest challenges faced by internet service providers, organizations and internet users in unison. Spam mails may be targeted, with a malicious intent or just as a commercial marketing activity - on the whole unwanted by everyone except the dispatcher. Spam filters continuously evolve as spammers go techno-savvy and creative. Machine learning algorithms have been popularly used for classifying and predicting mails as spam or ham (the good emails). This work presents a spam filter, BeaKS, with a focused preprocessing phase that weaves both the content of the email and two behavioral characteristics extracted from the email, to predict the category a mail belongs to: spam or ham. The accuracy of the proposed prediction model using Random Forests as the classifier is shown to be superior over other recent techniques. This approach is simple, easy to implement and reliable. © 2011 IEEE

An efficient prediction model for diabetic database using soft computing techniques

Organizations aim at harnessing predictive insights, using the vast real-time data stores that they have accumulated through the years, using data mining techniques. Health sector, has an extremely large source of digital data - patient-health related data-store, which can be effectively used for predictive analytics. This data, may consists of missing, incorrect and sometimes incomplete values sets that can have a detrimental effect on the decisions that are outcomes of data analytics. Using the PIMA Indians Diabetes dataset, we have proposed an efficient imputation method using a hybrid combination of CART and Genetic Algorithm, as a preprocessing step. The classical neural network model is used for prediction, on the preprocessed dataset. The accuracy achieved by the proposed model far exceeds the existing models, mainly because of the soft computing preprocessing adopted. This approach is simple, easy to understand and implement and practical in its approach. © 2009 Springer-Verlag Berlin Heidelberg

An efficient framework for prediction in healthcare data using soft computing techniques

Healthcare organizations aim at deriving valuable insights employing data mining and soft computing techniques on the vast data stores that have been accumulated over the years. This data however, might consist of missing, incorrect and most of the time, incomplete instances that can have a detrimental effect on the predictive analytics of the healthcare data. Preprocessing of this data, specifically the imputation of missing values offers a challenge for reliable modeling. This work presents a novel preprocessing phase with missing value imputation for both numerical and categorical data. A hybrid combination of Classification and Regression Trees (CART) and Genetic Algorithms to impute missing continuous values and Self Organizing Feature Maps (SOFM) to impute categorical values is adapted in this work. Further, Artificial Neural Networks (ANN) is used to validate the improved accuracy of prediction after imputation. To evaluate this model, we use PIMA Indians Diabetes Data set (PIDD), and Mammographic Mass Data (MMD). The accuracy of the proposed model that emphasizes on a preprocessing phase is shown to be superior over the existing techniques. This approach is simple, easy to implement and practically reliable. © 2011 Springer-Verlag