Steganography, the art of hiding of information in apparently innocuous objects or images, is a field with a rich heritage, and an area of rapid current development. This clear, self-contained guide shows you how to understand the building blocks of covert communication in digital media files and how to apply the techniques in practice, including those of steganalysis, the detection of steganography. Assuming only a basic knowledge in calculus and statistics, the book blends the various strands of steganography, including information theory, coding, signal estimation and detection, and statistical signal processing. Experiments on real media files demonstrate the performance of the techniques in real life, and most techniques are supplied with pseudo-code, making it easy to implement the algorithms. The book is ideal for students taking courses on steganography and information hiding, and is also a useful reference for engineers and practitioners working in media security and information assurance.

http://www.gbv.de/dms/ilmenau/toc/608077828.PDF

Steganography in Digital Media: Principles, Algorithms, and Applications

Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

A Survey on Visual Content-Based Video Indexing and Retrieval

Guest Editorial: Gesture and speech in interaction: An overview

Techniques for facilitating interaction with an application in a motion capture system allow a person to easily begin interacting without manual setup. A depth camera system tracks a person in physical space and evaluates the person's intent to engage with the application. Factors such as location, stance, movement and voice data can be evaluated. Absolute location in a field of view of the depth camera, and location relative to another person, can be evaluated. Stance can include facing a depth camera, indicating a willingness to interact. Movements can include moving toward or away from a central area in the physical space, walking through the field of view, and movements which occur while standing generally in one location, such as moving one's arms around, gesturing, or shifting weight from one foot to another. Voice data can include volume as well as words which are detected by speech recognition.

Recognizing user intent in motion capture system

In a method for delivering targeted television advertisements based on online behavior, IP addresses indicating online access devices and IP addresses indicating television set-top boxes are electronically associated for a multitude of users. Using user profile information derived from online activity from one of the online access IP addresses, a television advertisement is selected, such as by using behavioral targeting or demographic information, and automatically directed to the set-top box indicated by the set-top IP address associated with that online access IP address. Preferably neither the user profile information nor the electronic association of online access and set-top box IP addresses includes personally identifiable information.

Targeted television advertisements based on online behavior

A method for head pose estimation may include receiving block motion vectors for a frame of video from a block motion estimator, selecting at least one block for analysis, determining an average motion vector for the at least one selected block, combining the average motion vectors over time (all past frames of video) to determine an accumulated average motion vector, estimating the orientation of a user's head in the video frame based on the accumulated average motion vector, and outputting at least one parameter indicative of the estimated orientation.

Apparatus and Methods for Head Pose Estimation and Head Gesture Detection

Compact representations of video data greatly enhances efficient video browsing. Such representations provide the user with information about the content of the particular sequence being examined while preserving the essential message. We propose a method to automatically generate video summaries using transcripts obtained by automatic speech recognition. We divide the full program into segments based on pause detection and derive a score for each segment, based on the frequencies of the words and bigrams it contains. Then, a summary is generated by selecting the segments with the highest score to duration ratios while at the same time maximizing the coverage of the summary over the full program. We developed an experimental design and a user study to judge the quality of the generated video summaries. We compared the informativeness of the proposed algorithm with two other algorithms for three different programs. The results of the user study demonstrate that the proposed algorithm produces more informative summaries than the other two algorithms

Automated video program summarization using speech transcripts

Text data forms the largest bulk of digital data that people encounter and exchange daily. For this reason the potential usage of text data as a covert channel for secret communication is an imminent concern. Even though information hiding into natural language text has started to attract great interest, there has been no study on attacks against these applications. In this paper we examine the robustness of lexical steganography systems.In this paper we used a universal steganalysis method based on language models and support vector machines to differentiate sentences modified by a lexical steganography algorithm from unmodified sentences. The experimental accuracy of our method on classification of steganographically modified sentences was 84.9%. On classification of isolated sentences we obtained a high recall rate whereas the precision was low.

/pdf/attacks-on-lexical-natural-language-steganography-systems-2jic4lxbg4.pdf

Attacks on lexical natural language steganography systems

In this paper we propose a system for the analysis of user generated video (UGV). UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e., the ?camera person.? We exploit this structure by defining a new concept known as camera view for temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person's interests in the scene and can also attract the viewers' attention. We propose a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. In order to evaluate our methods we conducted several user studies. A subjective evaluation indicated that our system produces results that is consistent with viewers' preferences. We also examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement which is consistent with our location-based saliency map.

Camera Motion-Based Analysis of User Generated Video

A method for retrieving relevant stories from a collection of stories. The method comprises the steps of identifying at least one query term, applying a cooccurrence matrix to the query term to provide a list of query terms, determining if a story in the collection contains any terms on the list of query terms, and then increasing a relevance measure if the story does contain words on the list of query words. If the relevance measure is higher than a threshold, the story is added to a list of relevant stories.

Cuneyt M. Taskiran

Papers

Apparatus and Methods for Head Pose Estimation and Head Gesture Detection

Automated video program summarization using speech transcripts

Attacks on lexical natural language steganography systems

Camera Motion-Based Analysis of User Generated Video

Method for statistical text analysis