We show that a class of optical physical unclonable functions (PUFs) can be efficiently PAC-learned to arbitrary precision with arbitrarily high probability, even in the presence of intentionally injected noise, given access to polynomially many challenge-response pairs, under mild and practical assumptions about the distributions of the noise and challenge vectors. We motivate our analysis by identifying similarities between the integrated version of Pappu’s original optical PUF design and the post-quantum Learning with Errors (LWE) cryptosystem. We derive polynomial bounds for the required number of samples and the computational complexity of a linear regression algorithm, based on size parameters of the PUF, the distributions of the challenge and noise vectors, and the desired accuracy and probability of success of the regression algorithm. We use a similar analysis to that done by Bootle et al. [“LWE without modular reduction and improved side-channel attacks against BLISS,” in Advances in Cryptology – ASIACRYPT 2018], who demonstrated a learning attack on poorly implemented versions of LWE cryptosystems. This extends the results of Rührmair et al. [“Optical PUFs reloaded,” Cryptology ePrint Archive, 2013], who presented a theoretical framework showing that a subset of this class of PUFs is learnable in polynomial time in the absence of injected noise, under the assumption that the optics of the PUF were either linear or had negligible nonlinear effects. (Rührmair et al. also included an experimental validation of this technique, which of course included measurement uncertainty, demonstrating robustness to the presence of natural noise.) We recommend that the design of strong PUFs should be treated as a cryptographic engineering problem in physics, as PUF designs would benefit greatly from basing their physics and security on standard cryptographic assumptions. Finally, we identify future research directions, including suggestions for how to modify an LWE-based optical PUF design to better defend against cryptanalytic attacks.

We characterize a class of videos consisting of very small but potentially complicated motions. We find that in these scenes, linear appearance variations have a direct relationship to scene motions. We show how to interpret appearance variations captured through a PCA decomposition of the image set as a scene-specific non-parametric motion basis. We propose fast, robust tools for dense flow estimates that are effective in scenes with small motions and potentially large image noise. We show example results in a variety of applications, including motion segmentation and long-term point tracking.

We develop a framework to allow generic object detection algorithms to exploit geometric information commonly available to robot vision systems. Robot systems take pictures with calibrated cameras from known positions and may simultaneously capture depth measurements in the scene. This allows known constraints on the 3D size and position of objects to be translated into constraints on potential locations and scales of objects in the image, eliminating potentially expensive image operations for geometrically infeasible object locations. We show this integration to be very natural in the context of face detection and find that the computational effort of the standard Viola Jones face detector (as implemented in OpenCV) can be reduced by 85 percent with three times fewer false positives.

We describe a complete, end-to-end system for taking well-composed photographs using a mobile robot. The general scenario is a reception, or other event, where people are roaming around talking to each other. The robot serves as an "event photographer", roaming around the same space as the participants, periodically taking photographs. These images are then sent to a workstation where participants can print the photographs out, or email them.

No abstract available.

Article High-latency, low-bandwidth windowing in the Jupiter collaboration system Share on Authors: David A. Nichols Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CAView Profile , Pavel Curtis Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CAView Profile , Michael Dixon Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CAView Profile , John Lamping Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CAView Profile Authors Info & Claims UIST '95: Proceedings of the 8th annual ACM symposium on User interface and software technologyDecember 1995 Pages 111–120https://doi.org/10.1145/215585.215706Online:01 December 1995Publication History 99citation2,591DownloadsMetricsTotal Citations99Total Downloads2,591Last 12 Months44Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Two new parsing algorithms for context-free phrase structure grammars are presented which perform a bounded amount of processing per word per analysis path, independently of sentence length. They are thus capable of parsing in real-time in a parallel implementation which forks processors in response to non-deterministic choice points.