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

Implementasi Steganalisis dengan Menggunakan Metode BSM-SVM pada Steganografi Citra Digital

ABSTRAKSI: Steganografi atau teknik penyisipan data saat ini sering sekali digunakan oleh banyak pihak untuk berbagai kepentingan, salah satunya adalah untuk penyelundupan data. Oleh karena itu diperlukan suatu aplikasi yang dapat mendeteksi teknik steganografi tersebut sehingga diharapkan mampu meminimalisir upaya penyelundupan data rahasia.Steganalisis merupakan suatu disiplin ilmu yang mempelajari cara mendeteksi keberadaan teknik steganografi dalam suatu media tertentu. Salah satu teknik steganalisis ini adalah Binary Similarity Measures – Support Vector Machine (BSM-SVM) yang digunakan untuk mencari pola-pola tertentu pada suatu media pada level binary. Metode ini termasuk ke dalam metode blind steganalysis dimana metode ini mampu mendeteksi semua metode Steganografi dan pada semua format file dengan akurasi yang tinggi.Pada tugas akhir diimplementasikan metode BSM-SVM untuk melakukan steganalisis terhadap beberapa set citra digital dengan tujuan apakah metode ini bisa mendeteksi teknik steganografi LSB dan F5 pada format BMP dan JPG. Berdasarkan pengujian yang telah dilakukan terhadap citra digital, Algoritma BSM-SVM mampu mendeteksi metode LSB dan F5 dan memiliki nilai akurasi yang mencapai 77,28% untuk deteksi metode LSB dan 76,49% untuk deteksi metode F5. Metode ini juga mampu diterapkan pada format citra digital berupa JPG dan BMP dimana pada JPG akurasinya mencapai 77,02% dan pada BMP sebesar 76,75%.Kata Kunci : Kata Kunci : Citra Digital, Steganografi, Steganalisis, Binary Similarity Measures, Support Vector Machine.ABSTRACT: Nowadays, with little help from technology we can embed secret message into any digital media. Steganography is one of the technique that can be used to embed secret message into digital media. Sometimes these Steganography technique is used to do some illegal sharing activity. An application that is capable of detect this kind of secret message, are required in many cases. This application can be used to prevent the secret message from being spread publicly.Steganalysis is a science techniques used for detecting any steganographic message in any digital media. One of the steganalysis method is Binary Similarity Measures - Support Vector Machine. This method is classified as a blind steganalysis technique which means able to detect all steganography technique with high accuracy.Based on the testing result, the Binary Similarity Measures - Support Vector Machine algorithm has an accuracy value reaching up to 77,28% for detecting LSB method and 76,49% for detecting F5 method. This steganalysis technique also can be used for various digital image format like JPG and BMP. The accuracy for JPG is 77,02% and for BMP is 76,75%. And more bigger the embedded message filesize it will be easier for this technique to detect the message.Keyword: Image, Steganography, Steganalysis, Binary Similarity Measures, Support Vector Machine

Information similarity metrics in information security and forensics

We study two information similarity measures, relative entropy and the similarity metric, and methods for estimating them. Relative entropy can be readily estimated with existing algorithms based on compression. The similarity metric, based on algorithmic complexity, proves to be more difficult to estimate due to the fact that algorithmic complexity itself is not computable. We again turn to compression for estimating the similarity metric. Previous studies rely on the compression ratio as an indicator for choosing compressors to estimate the similarity metric. This assumption, however, is fundamentally flawed. We propose a new method to benchmark compressors for estimating the similarity metric. To demonstrate its use, we propose to quantify the security of a stegosystem using the similarity metric. Unlike other measures of steganographic security, the similarity metric is not only a true distance metric, but it is also universal in the sense that it is asymptotically minimal among all computable metrics between two objects. Therefore, it accounts for all similarities between two objects. In contrast, relative entropy, a widely accepted steganographic security definition, only takes into consideration the statistical similarity between two random variables. As an application, we present a general method for benchmarking stegosystems. The method is general in the sense that it is not restricted to any covertext medium and therefore, can be applied to a wide range of stegosystems. For demonstration, we analyze several image stegosystems using the newly proposed similarity metric as the security metric. The results show the true security limits of stegosystems regardless of the chosen security metric or the existence of steganalysis detectors. In other words, this makes it possible to show that a stegosystem with a large similarity metric is inherently insecure, even if it has not yet been broken

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

Machine learning based digital image forensics and steganalysis

The security and trustworthiness of digital images have become crucial issues due to the simplicity of malicious processing. Therefore, the research on image steganalysis (determining if a given image has secret information hidden inside) and image forensics (determining the origin and authenticity of a given image and revealing the processing history the image has gone through) has become crucial to the digital society. In this dissertation, the steganalysis and forensics of digital images are treated as pattern classification problems so as to make advanced machine learning (ML) methods applicable. Three topics are covered: (1) architectural design of convolutional neural networks (CNNs) for steganalysis, (2) statistical feature extraction for camera model classification, and (3) real-world tampering detection and localization. For covert communications, steganography is used to embed secret messages into images by altering pixel values slightly. Since advanced steganography alters the pixel values in the image regions that are hard to be detected, the traditional ML-based steganalytic methods heavily relied on sophisticated manual feature design have been pushed to the limit. To overcome this difficulty, in-depth studies are conducted and reported in this dissertation so as to move the success achieved by the CNNs in computer vision to steganalysis. The outcomes achieved and reported in this dissertation are: (1) a proposed CNN architecture incorporating the domain knowledge of steganography and steganalysis, and (2) ensemble methods of the CNNs for steganalysis. The proposed CNN is currently one of the best classifiers against steganography. Camera model classification from images aims at assigning a given image to its source capturing camera model based on the statistics of image pixel values. For this, two types of statistical features are designed to capture the traces left by in-camera image processing algorithms. The first is Markov transition probabilities modeling block-DCT coefficients for JPEG images; the second is based on histograms of local binary patterns obtained in both the spatial and wavelet domains. The designed features serve as the input to train support vector machines, which have the best classification performance at the time the features are proposed. The last part of this dissertation documents the solutions delivered by the author’s team to The First Image Forensics Challenge organized by the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. In the competition, all the fake images involved were doctored by popular image-editing software to simulate the real-world scenario of tampering detection (determine if a given image has been tampered or not) and localization (determine which pixels have been tampered). In Phase-1 of the Challenge, advanced steganalysis features were successfully migrated to tampering detection. In Phase-2 of the Challenge, an efficient copy-move detector equipped with PatchMatch as a fast approximate nearest neighbor searching method were developed to identify duplicated regions within images. With these tools, the author’s team won the runner-up prizes in both the two phases of the Challenge

Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page