User authentication systems are at an impasse. The most ubiquitous method -the password -has numerous problems, including susceptibility to unintentional exposure via phishing and cross-site password reuse. Second-factor authentication schemes have the potential to increase security but face usability and deployability challenges. For example, conventional second-factor schemes change the user authentication experience. Furthermore, while more secure than passwords, second-factor schemes still fail to provide sufficient protection against (single-use) phishing attacks.We present PhoneAuth, a system intended to provide security assurances comparable to or greater than that of conventional twofactor authentication systems while offering the same authentication experience as traditional passwords alone. Our work leverages the following key insights. First, a user's personal device (e.g., a phone) can communicate directly with the user's computer (and hence the remote web server) without any interaction with the user. Second, it is possible to provide a layered approach to security, whereby a web server can enact different policies depending on whether or not the user's personal device is present. We describe and evaluate our server-side, Chromium web browser, and Android phone implementations of PhoneAuth.
Deep learning approaches are now a popular choice in the field of automatic emotion recognition (AER) across various modalities. Due to the high costs of manually labeling human emotions however, the amount of available training data is relatively scarce in comparison to other tasks. To facilitate the learning process and reduce the necessary amount of training-data, modern approaches therefore often rely on leveraging knowledge from models that have already been trained on related tasks where data is available abundantly. In this work we introduce a novel approach to transfer learning, which addresses two shortcomings of traditional methods: The (partial) inheritance of the original models structure and the restriction to other neural network models as an input source. To this end we identify the parts in the input that have been relevant for the decision of the model we want to transfer knowledge from, and directly encode those relevant regions in the data on which we train our new model. To validate our approach we performed experiments on well-established datasets for the task of automatic facial expression recognition. The results of those experiments are suggesting that our approach helps to accelerate the learning process.
Underwater sounds provide essential information for marine researchers to study sea mammals. During long-term studies large amounts of sound signals are being recorded using hydrophones. To facilitate the time consuming process of manually evaluating the recorded data, computational systems are often employed. Recent approaches utilize Convolutional Neural Networks (CNNs) to analyze spectrograms extracted from the audio signal. In this paper we explore the potential of relevance analysis to enhance the performance of existing CNN approaches. For this purpose, we present a fusion system that utilizes intermediate outputs of three state of the art CNNs, which are fine tuned to recognize whale sounds in spectrograms. Hereby we use Explainable Artificial Intelligence (XAI) to asses the relevance of each feature within the obtained representations. Based on those relevance values, we create novel masking algorithms to extract significant subsets of respective representations. These subsets are used to train an ensemble of classification systems that are serving as input for the final fusion step. We observe that a classification system can benefit from the inclusion of Relevance-based Feature Masking in terms of improved performance and reduced input dimensionality. The presented work is part of the INTERSPEECH 2019 Computational Paralinguistics Challenge.
The ongoing rise of Generative Adversarial Networks is opening the possibility to create highly-realistic, natural looking images in various fields of application. One particular example is the generation of emotional human face images that can be applied to diverse use-cases such as automated avatar generation. However, most conditional approaches to create such emotional faces are addressing categorical emotional states, making smooth transitions between emotions difficult. In this work, we explore the possibilities of label interpolation in order to enhance a network that was trained on categorical emotions with the ability to generate face images that show emotions located in a continuous valence-arousal space.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.