Biblio

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. At the same time, there is an increasing need to share such video data across a wide spectrum of stakeholders including professionals, therapists and families facing similar challenges. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this paper, we propose a method of manipulating facial expression and body shape to conceal the identity of individuals while preserving the underlying affect states. The experiment results demonstrate the effectiveness of our method.

Spatial-multiplexing cameras have emerged as a promising alternative to classical imaging devices, often enabling acquisition of `more for less'. One popular architecture for spatial multiplexing is the single-pixel camera (SPC), which acquires coded measurements of the scene with pseudo-random spatial masks. Significant theoretical developments over the past few years provide a means for reconstruction of the original imagery from coded measurements at sub-Nyquist sampling rates. Yet, accurate reconstruction generally requires high measurement rates and high signal-to-noise ratios. In this paper, we enquire if one can perform high-level visual inference problems (e.g. face recognition or action recognition) from compressive cameras without the need for image reconstruction. This is an interesting question since in many practical scenarios, our goals extend beyond image reconstruction. However, most inference tasks often require non-linear features and it is not clear how to extract such features directly from compressed measurements. In this paper, we show that one can extract nontrivial correlational features directly without reconstruction of the imagery. As a specific example, we consider the problem of face recognition beyond the visible spectrum e.g in the short-wave infra-red region (SWIR) - where pixels are expensive. We base our framework on smashed filters which suggests that inner-products between high-dimensional signals can be computed in the compressive domain to a high degree of accuracy. We collect a new face image dataset of 30 subjects, obtained using an SPC. Using face recognition as an example, we show that one can indeed perform reconstruction-free inference with a very small loss of accuracy at very high compression ratios of 100 and more.

Advanced persistent threat (APT) is becoming a major threat to cyber security. As APT attacks are often launched by well funded entities that are persistent and stealthy in achieving their goals, they are highly challenging to combat in a cost-effective way. The situation becomes even worse when a sophisticated attacker is further assisted by an insider with privileged access to the inside information. Although stealthy attacks and insider threats have been considered separately in previous works, the coupling of the two is not well understood. As both types of threats are incentive driven, game theory provides a proper tool to understand the fundamental tradeoffs involved. In this paper, we propose the first three-player attacker-defender-insider game to model the strategic interactions among the three parties. Our game extends the two-player FlipIt game model for stealthy takeover by introducing an insider that can trade information to the attacker for a profit. We characterize the subgame perfect equilibria of the game with the defender as the leader and the attacker and the insider as the followers, under two different information trading processes. We make various observations and discuss approaches for achieving more efficient defense in the face of both APT and insider threats.

Descriptors such as local binary patterns perform well for face recognition. Searching large databases using such descriptors has been problematic due to the cost of the linear search, and the inadequate performance of existing indexing methods. We present Discrete Cosine Transform (DCT) hashing for creating index structures for face descriptors. Hashes play the role of keywords: an index is created, and queried to find the images most similar to the query image. Common hash suppression is used to improve retrieval efficiency and accuracy. Results are shown on a combination of six publicly available face databases (LFW, FERET, FEI, BioID, Multi-PIE, and RaFD). It is shown that DCT hashing has significantly better retrieval accuracy and it is more efficient compared to other popular state-of-the-art hash algorithms.
 

User authentication depends largely on the concept of passwords. However, users find it difficult to remember alphanumerical passwords over time. When user is required to choose a secure password, they tend to choose an easy, short and insecure password. Graphical password method is proposed as an alternative solution to text-based alphanumerical passwords. The reason of such proposal is that human brain is better in recognizing and memorizing pictures compared to traditional alphanumerical string. Therefore, in this paper, we propose a conceptual framework to better understand the user performance for new high-end graphical password method. Our proposed framework is based on hybrid approach combining different features into one. The user performance experimental analysis pointed out the effectiveness of the proposed framework.
 

Smart objects are small devices with limited system resources, typically made to fulfill a single simple task. By connecting smart objects and thus forming an Internet of Things, the devices can interact with each other and their users and support a new range of applications. Due to the limitations of smart objects, common security mechanisms are not easily applicable. Small message sizes and the lack of processing power severely limit the devices' ability to perform cryptographic operations. This paper introduces a protocol for delegating client authentication and authorization in a constrained environment. The protocol describes how to establish a secure channel based on symmetric cryptography between resource-constrained nodes in a cross-domain setting. A resource-constrained node can use this protocol to delegate authentication of communication peers and management of authorization information to a trusted host with less severe limitations regarding processing power and memory.