New steerable pyramid steganography algorithm resistant to the Fisher Linear Discriminant steganalysis

This paper describes a new steganography algorithm based on a steerable pyramid transform of a digital image and the steganalysis of the existence of secret messages hidden by this new method. The data embedding process uses the elements of a Lee and Chen steganography algorithm which is adapted to the steerable pyramid transform domain. This article describes the Fisher Linear Disriminant (FLD) analysis and its steganalysis application, too. The main part of the paper is the description of the conducted research and the results of FLD steganalysis of stegoimages produced by the new steganography algorithm

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

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

Over 725 steganography tools are available over the Internet, each providing a method for covert transmission of secret messages. This research presents four steganalysis advancements that result in an algorithm that identifies the steganalysis tool used to embed a secret message in a JPEG image file. The algorithm includes feature generation, feature preprocessing, multi-class classification and classifier fusion. The first contribution is a new feature generation method which is based on the decomposition of discrete cosine transform (DCT) coefficients used in the JPEG image encoder. The generated features are better suited to identifying discrepancies in each area of the decomposed DCT coefficients. Second, the classification accuracy is further improved with the development of a feature ranking technique in the preprocessing stage for the kernel Fisher s discriminant (KFD) and support vector machines (SVM) classifiers in the kernel space during the training process. Third, for the KFD and SVM two-class classifiers a classification tree is designed from the kernel space to provide a multi-class classification solution for both methods. Fourth, by analyzing a set of classifiers, signature detectors, and multi-class classification methods a classifier fusion system is developed to increase the detection accuracy of identifying the embedding method used in generating the steganography images. Based on classifying stego images created from research and commercial JPEG steganography techniques, F5, JP Hide, JSteg, Model-based, Model-based Version 1.2, OutGuess, Steganos, StegHide and UTSA embedding methods, the performance of the system shows a statistically significant increase in classification accuracy of 5%. In addition, this system provides a solution for identifying steganographic fingerprints as well as the ability to include future multi-class classification tools

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

Natural Image Statistics for Digital Image Forensics

We describe a set of natural image statistics that are built upon two multi-scale image decompositions, the quadrature mirror filter pyramid decomposition and the local angular harmonic decomposition. These image statistics consist of first- and higher-order statistics that capture certain statistical regularities of natural images. We propose to apply these image statistics, together with classification techniques, to three problems in digital image forensics: (1) differentiating photographic images from computer-generated photorealistic images, (2) generic steganalysis; (3) rebroadcast image detection. We also apply these image statistics to the traditional art authentication for forgery detection and identification of artists in an art work. For each application we show the effectiveness of these image statistics and analyze their sensitivity and robustness

Steganography and Steganalysis in Digital Multimedia: Hype or Hallelujah?

In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed.In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed

Performance improvement of JPEG2000 steganography using QIM

This paper presents a modified QIM-JPEG2000 steganography which improves the previous JPEG2000 steganography using quantization index modulation (QIM). Since after-embedding changes on file size and PSNR by the modified QIM-JPEG2000 are smaller than those by the previous QIM-JPEG2000, the modified QIM-JPEG2000 should be more secure than the previous QIM-JPEG2000.4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2008), 15-17, August, 2008, Harbin, Chin