Conceivable security risks and authentication techniques for smart devices

With the rapidly escalating use of smart devices and fraudulent transaction of users’ data from their devices, efficient and reliable techniques for authentication of the smart devices have become an obligatory issue. This paper reviews the security risks for mobile devices and studies several authentication techniques available for smart devices. The results from field studies enable a comparative evaluation of user-preferred authentication mechanisms and their opinions about reliability, biometric authentication and visual authentication techniques

Towards durable multistakeholder-generated solutions: The pilot application of a problem-oriented policy learning protocol to legality verification and community rights in Peru

This paper reports and reflects on the pilot application of an 11-step policy learning protocol that was developed by Cashore and Lupberger (2015) based on several years of Cashore’s multi-author collaborations. The protocol was applied for the first time in Peru in 2015 and 2016 by the IUFRO Working Party on Forest Policy Learning Architectures (hereinafter referred to as the project team). The protocol integrates insights from policy learning scholarship (Hall 1993, Sabatier 1999) with Bernstein and Cashore’s (2000, 2012) four pathways of influence framework. The pilot implementation in Peru focused on how global timber legality verification interventions might be harnessed to promote local land rights. Legality verification focuses attention on the checking and auditing of forest management units in order to verify that timber is harvested and traded in compliance with the law. We specifically asked: How can community legal ownership of, and access to, forestland and forest resources be enhanced? The protocol was designed as a dynamic tool, the implementation of which fosters iterative rather than linear processes. It directly integrated two objectives: 1) identifying the causal processes through which global governance initiatives might be harnessed to produce durable results ‘on the ground’; 2) generating insights and strategies in collaboration with relevant stakeholders. This paper reviews and critically evaluates our work in designing and piloting the protocol. We assess what seemed to work well and suggest modifications, including an original diagnostic framework for nurturing durable change. We also assess the implications of the pilot application of the protocol for policy implementation that works to enhance the influence of existing international policy instruments, rather than contributing to fragmentation and incoherence by creating new ones

Action Recognition in Video Using Sparse Coding and Relative Features

This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-of-the-art techniques by a large margin.Comment: Accepted to CVPR 201

Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks