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

Outlier Mining Methods Based on Graph Structure Analysis

Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.Peer ReviewedPostprint (published version

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial