J-M. Frahm

Bio: J-M. Frahm is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Camera resectioning & Pattern recognition (psychology). The author has an hindex of 3, co-authored 3 publications receiving 280 citations.

Simple calibration of non-overlapping cameras with a mirror

Abstract: Calibrating a network of cameras with non-overlapping views is an important and challenging problem in computer vision. In this paper, we present a novel technique for camera calibration using a planar mirror. We overcome the need for all cameras to see a common calibration object directly by allowing them to see it through a mirror. We use the fact that the mirrored views generate a family of mirrored camera poses that uniquely describe the real camera pose. Our method consists of the following two steps: (1) using standard calibration methods to find the internal and external parameters of a set of mirrored camera poses, (2) estimating the external parameters of the real cameras from their mirrored poses by formulating constraints between them. We demonstrate our method on real and synthetic data for camera clusters with small overlap between the views and non-overlapping views.

Security and usability challenges of moving-object CAPTCHAs: decoding codewords in motion

Abstract: We explore the robustness and usability of moving-image object recognition (video) captchas, designing and implementing automated attacks based on computer vision techniques. Our approach is suitable for broad classes of moving-image captchas involving rigid objects. We first present an attack that defeats instances of such a captcha (NuCaptcha) representing the state-of-the-art, involving dynamic text strings called codewords. We then consider design modifications to mitigate the attacks (e.g., overlapping characters more closely). We implement the modified captchas and test if designs modified for greater robustness maintain usability. Our lab-based studies show that the modified captchas fail to offer viable usability, even when the captcha strength is reduced below acceptable targets--signaling that the modified designs are not viable. We also implement and test another variant of moving text strings using the known emerging images idea. This variant is resilient to our attacks and also offers similar usability to commercially available approaches. We explain why fundamental elements of the emerging images concept resist our current attack where others fails.

The end is nigh: generic solving of text-based CAPTCHAs

Abstract: Over the last decade, it has become well-established that a captcha's ability to withstand automated solving lies in the difficulty of segmenting the image into individual characters. The standard approach to solving captchas automatically has been a sequential process wherein a segmentation algorithm splits the image into segments that contain individual characters, followed by a character recognition step that uses machine learning. While this approach has been effective against particular captcha schemes, its generality is limited by the segmentation step, which is hand-crafted to defeat the distortion at hand. No general algorithm is known for the character collapsing anti-segmentation technique used by most prominent real world captcha schemes. This paper introduces a novel approach to solving captchas in a single step that uses machine learning to attack the segmentation and the recognition problems simultaneously. Performing both operations jointly allows our algorithm to exploit information and context that is not available when they are done sequentially. At the same time, it removes the need for any hand-crafted component, making our approach generalize to new captcha schemes where the previous approach can not. We were able to solve all the real world captcha schemes we evaluated accurately enough to consider the scheme insecure in practice, including Yahoo (5.33%) and ReCaptcha (33.34%), without any adjustments to the algorithm or its parameters. Our success against the Baidu (38.68%) and CNN (51.09%) schemes that use occluding lines as well as character collapsing leads us to believe that our approach is able to defeat occluding lines in an equally general manner. The effectiveness and universality of our results suggests that combining segmentation and recognition is the next evolution of catpcha solving, and that it supersedes the sequential approach used in earlier works. More generally, our approach raises questions about how to develop sufficiently secure captchas in the future.

Moving face spoofing detection via 3D projective invariants

Abstract: Face recognition provides many advantages compared with other available biometrics, but it is particularly subject to spoofing. The most accurate methods in literature addressing this problem, rely on the estimation of the three-dimensionality of faces, which heavily increase the whole cost of the system. This paper proposes an effective and efficient solution to problem of face spoofing. Starting from a set of automatically located facial points, we exploit geometric invariants for detecting replay attacks. The presented results demonstrate the effectiveness and efficiency of the proposed indices.