Phishing has become a crippling problem for many of today's internet users. Despite innovations in preventative measures, phishing has evolved to become very hard to detect. Determining whether a particular site is a phishing site or not is difficult, causing many inexperienced users to fall victim. Phishing site operators often actively prevent anti-phishing measures by detecting and blacklisting the IP addresses that try to probe them. In this paper, we propose using a cloud infrastructure to mask our identity from assumed phishing sites through the use of virtual machines spread across many geographic regions. Our system presents different personas and user behavior to the phishing sites by using different IP addresses and different browsing configurations. By running a 10-day probe experiment against real phishing site, we show the effectiveness of this approach in preventing detection and blocking of anti-phishing probes by the phishing site operators.

We present an algorithm for identifying and tracking independently moving rigid objects from optical flow. Some previous attempts at segmentation via optical flow have focused on finding discontinuities in the flow field. While discontinuities do indicate a change in scene depth, they do not in general signal a boundary between two separate objects. The proposed method uses the fact that each independently moving object has a unique epipolar constraint associated with its motion. Thus motion discontinuities based on self-occlusion can be distinguished from those due to separate objects. The use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. The algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. No camera calibration parameters are required. A Kalman filter based approach is used for tracking motion parameters with time.

This paper describes two projects applying computer vision to Intelligent Vehicle Highway Systems. The first project has resulted in the development of a system for monitoring traffic scenes using video information. The objective is to estimate traffic parameters such as flow rates, speeds and link travel times, as well as to detect quickly disruptive incidents such as stalled vehicles and accidents. The second project is aimed at developing vision as a sensor technology for vehicle control. The novel feature of this project, compared to most previous approaches, is the extensive use of binocular stereopsis. First, it provides information for obstacle detection, grouping, and range estimation which is directly used for longitudinal control. Secondly, the obstacle-ground separation enables robust localization of partially occluded lane boundaries as v/ell as the dynamic update of camera rig parameters to deal with vibrations and vertical road curvature. 1

Proposes a new approach for vision-based longitudinal and lateral vehicle control. The novel feature of this approach is the use of binocular vision. We integrate two modules consisting of a new, domain-specific, efficient binocular stereo algorithm, and a lane marker detection algorithm, and show that the integration results in a improved performance for each of the modules. Longitudinal control is supported by detecting and measuring the distances to leading vehicles using binocular stereo. The knowledge of the camera geometry with respect to the locally planar road is used to map the images of the road plane in the two camera views into alignment. This allows us to separate image features into those lying in the road plane, e.g. lane markers, and those due to other objects which are dynamically integrated into an obstacle map. Therefore, in contrast with the previous work, we can cope with the difficulties arising from occlusion of lane markers by other vehicles. The detection and measurement of the lane markers provides us with the positional parameters and the road curvature which are needed for lateral vehicle control. Moreover, this information is also used to update the camera geometry with respect to the road, therefore allowing us to cope with the problem of vibrations and road inclination to obtain consistent results from binocular stereo.>

We present an algorithm for identifying and tracking independently moving rigid objects from optical flow. The proposed method uses the fact that each distinct object has a unique epipolar constraint associated with its motion. This is in contrast to using local optical flow information for segmentation. Thus motion discontinuities based on self-occlusion are distinguished from those due to separate objects. The use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. The segmentation problem is formulated as a scene partitioning problem and a statistic-based algorithm which uses only nearest neighbor interactions and a finite number of iterations is developed. A Kalman filter based approach is used for tracking motion parameters with time. The algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. No camera calibration parameters are required.>

Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehicle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled the authors to develop a system for detailed, reliable traffic scene analysis. The machine vision component of the system employs a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, the authors discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype. Preliminary results of an implementation on special purpose hardware using C-40 Digital Signal Processors show that near real-time performance can be achieved without further improvements.

The authors developed an algorithm for computing optical flow in a differential framework. The image sequence is first convolved with a set of linear, separable spatiotemporal filters similar to those that have been used in other early vision. The brightness constancy constraint can then be applied to each of the resulting images, giving in general, an overdetermined system of equations for the optical flow at each pixel. There are three principal sources of error: (a) stochastic due to sensor noise, (b) systematic errors in the presence of large displacements, and (c) errors due to failure of the brightness constancy model. The analysis of these errors leads to development of an algorithm based on a robust version of total least squares. Each optical flow vector computed has an associated reliability measure which can be used in subsequent processing. The performance of the algorithm on the data set used by J. Barron et al. (1992) compares favorably with other techniques. In addition to being separable, the filters used are also causal, incorporating only past time frames. The algorithm is fully parallel and has been implemented on a multipleprocessor machine.>