Find the secret to improve the quality of life by reading this seeing the unseen. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.

Seeing the unseen.

With the development of modern steganography technologies, steganalysis has been a new research topic in the field of information security. Since JPEG images have been widely used in our daily life, the steganalysis for JPEG images becomes very important and significant. This paper propose a new steganalysis method based on sparse representation, intending to overcome the shortcomings of traditional classifiers in the field of universal steganalysis for JPEG images. Experimental results show that, comparing with the universal steganalysis method for JPEG stego images based on SVM, our method improves detection accuracy to some extent, and can avoid "over-fitting" problem in the process of classification. Experimental results also prove that our method is more robust than SVM when the detection images meet with Gaussian noises or Salt-Pepper noise.

A Universal Digital Image Steganalysis Method Based on Sparse Representation

Steganografi adalah praktik penyembunyian informasi dengan cara meyisipkan pesan rahasia kedalam media lain atau dikenal sebagai file carrier seperti citra digital, audio, video ataupun IP header. Pengawasan steganografi sulit untuk dilakukan sehingga dapat disalahgunakan oleh pihak kriminal dalam menyembunyikan jejaknya, pernah ada dugaan bahwa teroris menerapkan steganografi dalam berkomunikasi. Oleh sebab itu, perlu adanya sistem yang dapat mengatasi hal tersebut. Teknik mendeteksi keberadaan pesan rahasia pada sebuah file dikenal sebagai steganalisis. Metode steganalisis berbeda untuk setiap file carrier yang digunakan. IDIH merupakan salah satu metode steganalisis yang digunakan untuk mengidentifikasi steganografi dengan file carrier berupa citra digital. IDIH merupakan improvisasi dari metode sebelumnya yaitu DIH yang pertama kali diperkenalkan oleh T. Zhang et al. Pada metode DIH, histogram LSB plane sebelum dan setelah diubah menjadi 0 dibuat dari file citra yang diindikasi memiliki pesan rahasia untuk mendapatkan rasio embedding pesan. Kemudian ditambahkan skema baru metode IDIH dalam mencari nilai error untuk menghitung rasio embedding pesan termodifikasi yang menghasilkan estimasi panjang pesan rahasia yang lebih akurat. Pada makalah ini, hasil yang dicapai adalah performa sistem yang dilihat dari tingkat akurasinya berdasarkan nilai estimasi berupa persentase panjang pesan dari citra digital yang diindikasi sebagai sebuah stego-file.

Implementasi teknik steganalisis menggunakan metode improvement difference image histogram pada steganografi lsb

Abstract Novel image steganalytic method used to detection of secret message in static images is introduced in this paper. This method is based on statistical steganalysis (SS), where statistical vector is composed by 285 statistical features (parameters) extracted from DCT (Discrete Cosine Transformation) domain and 46 features extracted mainly from DWT (Discrete Wavelet Transformation) domain. Classification process was realized by Ensemble classifier that was helpful in reduction of computational and time complexity. Proposed steganalytic method was verified by detection of popular image steganographic methods. Novel method was also compared with existing steganalytic methods by overall detection accuracy of a secret message.

/pdf/universal-statistical-steganalytic-method-2c62fg3j4d.pdf

Universal statistical steganalytic method

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.

https://journals.umcs.pl/ai/article/download/3360/2554

Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files

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Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files