Steganographic Generative Adversarial Networks

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training (NIPS 2016, Barcelona, Spain

DETERMINATION OF THE ERROR OF MEASURING THE HEIGHTS OF THE OBJECTS DURING THE AUTOMATIC PROCESSING OF STEREO PICTURES

Currently, information on the spatial description of objects is used in many areas of human activity. One of these types of information is the coordinates of objects. Such data are used in cartography, in the construction of digital maps and 3D models, for the operation of navigation aids, etc. In the automated creation of digital models of terrain relief, one of the main qualitative indicators is the accuracy of determining the height of objects. The main influence on this indicator is made by the parallax measurement error when processing stereo images. To obtain a formula for calculating the accuracy of measuring the height of objects, let’s use the expansion of the function in a Taylor series. Using the Cramer-Rao formula for the potential accuracy of measuring the coordinates of the image of the object in the image, the Fourier transform and Parseval's equality, the formula for the potential accuracy of combining stereo images (parallax measurements) is obtained. The analysis of the obtained formulas shows that the image alignment accuracy deteriorates with an increase in the noise power spectral density in the first and second images and a decrease in the similarity of one image to another, as well as with a decrease in the effective width of the mutual spatial spectrum of stereo images. As the value of the stereo recognition basis increases, the error in measuring the heights of objects first improves, and then worsens. This deterioration is due to the fact that stereo pair images are obtained from different spatial points and at the same time perspective distortions and distortions in relief appear on the images. Accordingly, with an increase in the basis of shooting, these distortions will increase. This approach can be used when planning the mode of stereo shooting and equipment for removing the earth's surface for mapping, obtaining 3D models, etc

Efficient steganography detection by means of compression-based integral classifier

Предлагается концепция интегрального классификатора, предназначенного для повышения точности методов стегоанализа, которые базируются на машинном обучении. Вместо одиночного классификатора, принимающего решение о пустоте или заполненности контейнера, предлагается обучать набор классификаторов, каждый из которых предназначен для обработки контейнеров с определёнными свойствами. В качестве реализации данной концепции представлен интегральный классификатор, основанный на сжатии данных, что подразумевает выбор отдельного классификатора из набора на основе коэффициентов сжатия контейнеров. Эффективность предлагаемого классификатора для решения задачи обнаружения скрытой информации экспериментально продемонстрирована для современных методов адаптивного внедрения HUGO, WOW и S-UNIWARD на изображениях-контейнерах из известной базы BOSSbase 1.01. Показано, что в зависимости от метода внедрения и количества скрываемой информации ошибку обнаружения можно снизить на 0,05-0,16 по сравнению с лучшими из известных результатов