Universal Image Steganalytic Method

In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods

Analysis of MHPDM algorithm for data hiding in JPEG images

In the recent years, there has been a great deal of interest in developing a secure algorithm for hiding information in images, or steganography. There has also been a lot of research in steganalysis of images, which deals with the detection of hidden information in supposedly natural images. The first section of this thesis reviews the steganography algorithms and steganalysis techniques developed in the last few years. It discusses the breadth of steganographic algorithms and steganalytic techniques, starting with the earliest, based on LSB flipping of the DCT coefficients, to more recent and sophisticated algorithms for data hiding and equally clever steganalytic techniques. The next section focuses on the steganographic algorithm, MHPDM which was first developed by Eggers and then modified by Tzschoppe, Bauml, Huber and Kaup. The MHPDM algorithm preserves the histogram of the stego image and is thus perfectly secure in terms of Cachin\u27s security definition. The MHPDM algorithm is explained in detail and implemented in MATLAB. It is then tested on numerous images and steganalysed using Dr. Fridrich\u27s recent feature-based steganalytic technique. The thesis concludes with observations about the detectibility of MHPDM using feature-based steganalysis for different payloads (embedded message lengths)

Steganalysis Techniques: A Comparative Study

Steganography is the art of hiding information within cover objects like images or audio/video files. It has been widely reported that there has been a surge in the use of steganography for criminal activities and therefore, implementing effective detection techniques is an essential task in digital forensics. Unfortunately, building a single effective detection technique still remains one of the biggest challenges. This report presents a comparative study of three steganalysis techniques. We investigated and compared the performances of each technique in the detection of embedding methods considered. Based on the results of our analysis, we provide information as to which specific steganalysis technique needs to be used for a particular steganographic method. Finally, we propose a procedure which may help a forensic examiner to decide an order in which different steganalysis techniques need to be considered in the detection process to achieve the best detection results in terms of both time and accuracy

Steganalysis Techniques: A Comparative Study

Steganography is the art of hiding information within cover objects like images or audio/video files. It has been widely reported that there has been a surge in the use of steganography for criminal activities and therefore, implementing effective detection techniques is an essential task in digital forensics. Unfortunately, building a single effective detection technique still remains one of the biggest challenges. This report presents a comparative study of three steganalysis techniques. We investigated and compared the performances of each technique in the detection of embedding methods considered. Based on the results of our analysis, we provide information as to which specific steganalysis technique needs to be used for a particular steganographic method. Finally, we propose a procedure which may help a forensic examiner to decide an order in which different steganalysis techniques need to be considered in the detection process to achieve the best detection results in terms of both time and accuracy

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

Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files

Steganalysis is the art of detecting the presence of hidden data in files. In the last few years, there have been a lot of methods provided for steganalysis. Each method gives a good result depending on the hiding method. This paper aims at the evaluation of five universal steganalysis techniques which are “Wavelet based steganalysis”, “Feature Based Steganalysis”, “Moments of characteristic function using wavelet decomposition based steganalysis”, “Empirical Transition Matrix in DCT Domain based steganalysis”, and “Statistical Moment using jpeg2D array and 2D characteristic function”. A large Dataset of Images -1000 images- are subjected to three types of steganographic techniques which are “Outguess”, “F5” and “Model Based” with the embedding rate of 0.05, 0.1, and 0.2. It was followed by extracting the steganalysis feature used by each steganalysis technique for the stego images as well as the cover image. Then half of the images are devoted to train the classifier. The Support vector machine with a linear kernel is used in this study. The trained classifier is then used to test the other half of images, and the reading is reported The “Empirical Transition Matrix in DCT Domain based steganalysis” achieves the highest values among all the properties measured and it becomes the first choice for the universal steganalysis technique