9658544
Effect of Malicious Synchronization
We study the impact of malicious synchronization on computer systems that serve customers periodically. Systems supporting automatic periodic updates are common in web servers providing regular news update, sports scores or stock quotes. Our study focuses on the possibility of launching an effective low rate attack on the server to degrade performance measured in terms of longer processing time and request drops due to timeouts. The attackers are assumed to behave like normal users and send one request per update cycle. The only parameter utilized in the attack is the timing of the requests sent. By exploiting the periodic nature of the updates, a small number of attackers can herd users' update requests to a cluster and arrive in a short period of time. Herding can be used to discourage new users from joining the system and to modify the user arrival distribution, so that the subsequent burst attack will be effective. While the herding based attacks can be launched with a small amount of resource, they can be easily prevented by adding a small random component to the length of the update interval.
6661888
A wavelet approach to foveating images
Motivated by applications of foveated images in visualization, we introduce the foveation transform of an image. We study the basic properties of these transforms using the multiresolution framework of Mallat. We also consider practical methods of realizing such transforms. In particular, we introduce a new method for foveating images based on wavelets. Preliminary experimental results are shown.
11249256
A unified framework for resolving ambiguity in copy detection
Copy detection is an important component of digital rights management and can be implemented using a retrieval-based approach. Under this approach, a query image, suspected to be a copy, is compared against all the images in the owner database. The comparison is done based on a distance metric in feature space. The performance of such a system depends on the mutual separation of the feature representation of the images in the database. In this paper we propose a framework that increases this mutual separation by literally shifting them away from each other. The idea of modifying the features derives its inspiration from the field of watermarking. It is also important to make sure that the semantics of the images do not change after modification. Thus the focus of this paper is on how to modify the images in the database, so that the mutual separation between the images in feature space is above a certain threshold and the distortion induced is minimized. This problem can be formulated as a non-convex optimization problem which is difficult to solve. We propose a restriction of the problem and solve it using second-order cone programming. We present a practical implementation of our framework, named RAM, which uses AFMT as the feature representation. We conduct experiments to test the performance of RAM.
2005681
Hiding Secret Points Amidst Chaff
Motivated by the representation of biometric and multimedia objects, we consider the problem of hiding noisy point-sets using a secure sketch. A point-set X consists of s points from a d-dimensional discrete domain [0,N – 1]d. Under permissible noises, for every point $\left \langle x_1,..,x_d\right \rangle \in X$, each xi may be perturbed by a value of at most δ. In addition, at most t points in X may be replaced by other points in [0,N – 1]d. Given an original X, we want to compute a secure sketch P. A known method constructs the sketch by adding a set of random points R, and the description of (X∪R) serves as part of the sketch. However, the dependencies among the random points are difficult to analyze, and there is no known non-trivial bound on the entropy loss. In this paper, we first give a general method to generate R and show that the entropy loss of (X∪R) is at most s(dlogΔ+ d + 0.443), where Δ= 2δ+1. We next give improved schemes for d = 1, and special cases for d = 2. Such improvements are achieved by pre-rounding, and careful partition of the domains into cells. It is possible to make our sketch short, and avoid using randomness during construction. We also give a method in d = 1 to demonstrate that, using the size of R as the security measure would be misleading.
3079245
Statistics- and Spatiality-Based Feature Distance Measure for Error Resilient Image Authentication
Content-based image authentication typically assesses authenticity based on a distance measure between the image to be tested and its original. Commonly employed distance measures such as the Minkowski measures (including Hamming and Euclidean distances) may not be adequate for content-based image authentication since they do not exploit statistical and spatial properties in features. This paper proposes a feature distance measure for content-based image authentication based on statistical and spatial properties of the feature differences. The proposed statistics- and spatiality-based measure (SSM) is motivated by an observation that most malicious manipulations are localized whereas acceptable manipulations result in global distortions. A statistical measure, kurtosis, is used to assess the shape of the feature difference distribution; a spatial measure, the maximum connected component size, is used to assess the degree of object concentration of the feature differences. The experimental results have confirmed that our proposed measure is better than previous measures in distinguishing malicious manipulations from acceptable ones.
42674644
Shortest Path amidst Disc Obstacles Is Computable
An open question in Exact Geometric Computation is whether there are transcendental computations that can be made "geometrically exact". Perhaps the simplest such problem in computational geometry is that of computing the shortest obstacle-avoiding path between two points p,q in the plane, where the obstacles are a collection of n discs. This problem can be solved in O(n2log n) time in the Real RAM model, but nothing was known about its computability in the standard (Turing) model of computation. We first give a direct proof of the Turing-computability of this problem, provided the radii of the discs are rationally related. We make the usual assumption that the numerical input data are real algebraic numbers. By appealing to effective bounds from transcendental number theory, we further show a single-exponential time upper bound when the input numbers are rational. Our result appears to be the first example of a non-algebraic combinatorial problem which is shown computable. It is also a rare example of transcendental number theory yielding positive computational results.
6693066
Nnew: nearest neighbor expansion by weighting in image database retrieval
Various systems have been developed for supporting content-based image retrieval. Most systems make very strong assumptions in modeling users’ query concepts. However, since the information need of users can be very diverse, these assumptions may not always hold and hence can lead to poor search results. For instance, if a system assumes that the query-concept is convex but a user issues a disjunctive query, and vice versa, the search result cannot be satisfactory. In this study, we propose a method that can approximate more complex (non-convex and disjunctive) query concepts. Our method uses intelligent modeling and learning to increase query speed and accuracy. Empirical results show that our method converges consistently faster than some traditional approaches on different datasets.
14716350
Why one example is not enough for an image query
For over a decade, query-by-one-example has been a popular query paradigm for multimedia information retrieval. We show, by analyzing feature-to-semantics mapping, that such a paradigm cannot realistically lead to a scalable, satisfactory query performance. More specifically, we cluster a small image dataset based on the images' perceptual features, and show that these image clusters are not coherent to the semantic categories of the images. Though some image categories are well separated from the others in the input space formed by the perceptual features, most categories are colocated in more than one cluster. For a query-concept that is mixed with others in a number of clusters, the query-by-one-example paradigm simply lacks information to identify clearly the target query-concept, and hence cannot achieve satisfactory query results.
46114840
An Architecture of a Web-Based Collaborative Image Search Engine
We present a perception-based paradigm for image retrieval. The central component of this paradigm is a query-concept learner, which can learn users' subjective query concepts through an intelligent sampling process. We show that the learner can collect user feedback and use it to perform collaborative image annotation in addition to learning subjective query concepts. On the one hand, the improved annotation can help provide better initial keyword-search results to seed perception-based image retrieval. On the other hand, the more effective image-research results can further refine annotation quality. The users of the system collaboratively help improve search quality through the query-concept learner. Our empirical results show that an image retrieval system powered by this perception-based paradigm performs significantly better than traditional systems in search accuracy, in multimodal integration, and in capability for personalization.
38298301
Hybrid Learning Schemes for Multimedia Information Retrieval
Traditional database systems assume that precise query concepts can be specified by users (for example, by using query languages). For many search tasks, however, a query concept is hard to articulate, and articulation can be subjective. Most users would find it hard to describe an image or a music query in low-level perceptual features. We believe that one desirable paradigm for search engines is to mine (i.e., to learn) users' query concepts through active learning. In this paper, we formulate the query-concept learning problem as finding a binary classifier that separates relevant objects from those that are irrelevant to the query concept. We propose two hybrid algorithms, pipeline learning, and co-training, that are built on top of two active learning algorithms. Our empirical study shows that even when the feature dimension is very high and target concepts are very specific, the hybrid algorithms can grasp a complex query concept in a small number of user iterations.
6158088
Toward building a robust and intelligent video surveillance system: a case study
With the proliferation of inexpensive cameras, availability of large-capacity disk storage, and the ubiquitous presence of high-speed, broad-band communication networks, it is now economically and technically feasible to construct and deploy multi-camera video surveillance systems. In line with this is the need of intelligent, robust, (semi-)automated video analysis paradigms to assist the operators in scene analysis and event classification. In this paper, we summarize our current research work toward realizing such a multi-camera video surveillance system. Interested readers can visit our web sites http://www.ece.ucsb.edu/~echang and http://www.cs.ucsb.edu/ ~yfwang for more information