Narrow Results

This paper presents methods for performing steganography and steganalysis using a statistical model of the cover medium. The methodology is general, and can be applied to virtually any type of media. It provides answers for some fundamental questions that have not been fully addressed by previous steganographic methods, such as how large a message can be hidden without risking detection by certain statistical methods, and how to achieve this maximum capacity. Current steganographic methods have been shown to be insecure against simple statistical attacks. Using the model-based methodology, an example steganography method is proposed for JPEG images that achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks. A method is also described for defending against "blockiness" steganalysis attacks. Finally, a model-based steganalysis method is presented for estimating the length of messages hidden with Jsteg in JPEG images.

This paper introduces a graphical user interface approach to facilitate an efficient and timely generation of statistic data from input videos. By means of a carefully-designed graphical user interface, users can interactively add in various kinds of markers, known as the statistic inducers, on the screen of an input video to specify the areas of interest corresponding to the locations of relevant events. These inducers are in the form of two-dimensional points, lines, polygons, and grids, and can be put on the video screen with great ease. Using these inducers, we not only can efficiently customize the system for a given statistic generation task; in addition, we can also precisely constrain the time-consuming space-time video analysis process (as well as any additional analysis process like optical flow computation or object recognition) on the user-specified areas. To demonstrate the efficacy of the proposed approach, we developed a prototypic system and experimented it in two different statistic generation cases: dormitory light switching and road traffic. In both cases, we just need a few minutes of UI customization time to set up the inducers; once this is done, timely statistics can be automatically generated subsequently.