1,720,972 research outputs found
Joint Detection-Estimation Games for Sensitivity Analysis Attacks
Get PDFABSTRACT 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
On the fundamental tradeoff between watermark detection performance and robustness against sensitivity analysis attacks
No full text
AUB image-based guide - by Hani Abdallah Masri.
No full textProject (M.S.)--American University of Beirut, Dept. of Computer Science, 2012.;"Advisor : Dr. Maha El-Choubassi, Assistant Professor, Computer Science Committee Member : Dr. George Turkiyyah, Professor, Computer Science."Includes bibliographical references (leaves 24-25)AUB has one of the oldest and most beautiful university campuses in the region. We combine computer vision and web services techniques to create a compelling application that tells the story of AUB buildings from just a picture. In more details, we have
New sensitivity analysis attack
No full textThe 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
Novel algorithms for sensitivity analysis attacks
No full textSensitivity analysis attacks constitute a powerful family of watermark “removal ” attacks. They exploit a vulnerability in some watermarking protocols: the attacker’s unlimited access to the watermark detector. This paper proposes a mathematical framework for designing sensitivity analysis attacks and focuses on additive spread spectrum embedding schemes. The detectors under attack range in complexity from basic correlation detectors to normalized correlation detectors and maximum likelihood (ML) detectors. The new algorithms precisely estimate and then eliminate the watermark from the watermarked signal. This is done by exploiting geometric properties of the detection boundary and the information leaked by the detector. Several important extensions are presented, including the case of a partially unknown detection function, and the case of constrained detector inputs. In contrast with previous art, our algorithms are noniterative and require at most O(n) detection operations in order to estimate the watermark, where n is the dimension of the signal. The cost of each detection operation is O(n), hence the algorithms can be executed in quadratic time. The method is illustrated with an application to image watermarking using an ML detector based on a generalized Gaussian model for images
- …
