The widespread success of Kinect enables users to acquire both image and depth information with satisfying
accuracy at relatively low cost. We leverage the Kinect output to efficiently and accurately estimate the camera pose in presence of rotation, translation, or both. The applications of our algorithm are vast ranging from camera tracking, to 3D points clouds registration, and video stabilization. The state-of-the-art approach uses point correspondences for estimating the pose. More explicitly, it extracts point features from images, e.g., SURF or SIFT, and builds their descriptors, and matches features from different images to obtain point correspondences. However, while features-based approaches are widely used, they perform poorly in scenes lacking texture due to scarcity of features or in scenes with repetitive structure due to false correspondences. Our algorithm is intensity-based and requires neither point features’ extraction, nor descriptors’ generation/matching. Due to absence of depth, the intensity-based approach alone cannot handle camera translation. With Kinect capturing both image and depth frames, we extend the intensity-based algorithm to estimate the camera pose in case of both 3D rotation and translation. The results are quite promising.
Sensitivity analysis attacks aim at estimating a watermark from multiple observations of the detector's output.
Subsequently, the attacker removes the estimated watermark from the watermarked signal. In order to measure
the vulnerability of a detector against such attacks, we evaluate the fundamental performance limits for the
attacker's estimation problem. The inverse of the Fisher information matrix provides a bound on the covariance
matrix of the estimation error. A general strategy for the attacker is to select the distribution of auxiliary test
signals that minimizes the trace of the inverse Fisher information matrix. The watermark detector must trade off
two conflicting requirements: (1) reliability, and (2) security against sensitivity attacks. We explore this tradeoff
and design the detection function that maximizes the trace of the attacker's inverse Fisher information matrix
while simultaneously guaranteeing a bound on the error probability. Game theory is the natural framework to
study this problem, and considerable insights emerge from this analysis.
Despite their popularity, spread spectrum techniques have been proven to be vulnerable to sensitivity analysis attacks. Moreover, the number of detection operations needed by the attacker to estimate the watermark is generally linear in the size of the signal available to him. This holds not only for a simple correlation detector, but also for a wide class of detectors. Therefore there is a vital need for more secure detection methods. In this paper, we propose a randomized detection method that increases the robustness of spread spectrum embedding schemes. However, this is achieved at the expense of detection performance. For this purpose, we provide a framework to study the tradeoff between these two factors using classical detection-theoretic tools: large deviation analysis and Chernoff bounds. To gain more insight into the practical value of this framework, we apply it to image signals, for which "good" statistical models are available.
Proc. SPIE. 5681, Security, Steganography, and Watermarking of Multimedia Contents VII
KEYWORDS: Statistical analysis, Detection and tracking algorithms, Sensors, Digital watermarking, Electroluminescence, Multimedia, Algorithm development, Signal detection, Stochastic processes, Information security
The sensitivity analysis attacks by Kalker et al. constitute a
known family of watermark removal attacks exploiting a vulnerability in some watermarking protocols: the attacker's unlimited access to the watermark detector. In this paper, a new attack on spread spectrum schemes is designed. We first examine one of Kalker's algorithms and prove its convergence using the law of large numbers, which gives more insight into the problem. Next, a new algorithm is presented and compared to existing ones. Various detection algorithms are considered including correlation detectors and normalized correlation detectors, as well as other, more complicated algorithms. Our algorithm is noniterative and requires at most n+1 operations, where n is the dimension of the signal. Moreover, the new approach directly estimates the watermark by exploiting the simple geometry of the detection boundary and the information leaked by the detector.