243 research outputs found

    A Multi-Sensor System for Enhancing Situational Awareness and Stress Management for People with ASD in the Workplace and in Everyday Life

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    Autism spectrum disorders (ASD) present challenges for affected people at work and in everyday life. The barrier increases further with changing environmental situations. Deviations in factors like lighting or sound may lead to increased stress. The intervention plans to instil positive behaviour support (PBS) suggest that a customised environment can minimise the impacts due to these variations. This work proposes a novel framework which leverages the information from multi-sensor channels in a combined manner to customise the environment so that situational awareness (SA) can be improved. The proposed framework allows for monitoring the environment by combining the information from different sensor channels including both personal sensors (i.e. on board of a mobile device) as well as environmental sensors/actuators (i.e. embedded in smart-buildings). In this preliminary work, the system architecture is introduced. To demonstrate the potential of the proposed system, a case study is also considered through the development of a prototype for a mobile application and by reporting results on a scale model of a smart workplace with customisable environment

    Improved Dynamic Response of Buck Converter using Fuzzy Controller

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    This paper presents comparative performance of Buck Converter in both open as well as closed loop. With the help state equations Mathematical Model of Buck Converter is designed in MATLAB/SIMULINK. The obtained output response of open loop Buck Converter is sluggish, not optimum and consists of peak overshoots. PI Controller is used in closed loop model of Buck Converter. Output Voltage is compared with a reference signal, which then is processed through a controller (PI/Fuzzy). The obtained signal is superimposed with a carrier signal and given to switching device used. To further optimize the performance of Buck Converter and eliminate the peak overshoot present in output response, Fuzzy controller is used. Here Suguno type Fuzzy is used

    Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network

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    Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns. Detecting morphing attacks stemming from face images of newborn is important to avoid unwanted consequences, both for security and society. In this paper, we present a new reference-based/Differential Morphing Attack Detection (MAD) method to detect newborn morphing images using Wavelet Scattering Network (WSN). We propose a two-layer WSN with 250 ×\times 250 pixels and six rotations of wavelets per layer, resulting in 577 paths. The proposed approach is validated on a dataset of 852 bona fide images and 2460 morphing images constructed using face images of 42 unique newborns. The obtained results indicate a gain of over 10\% in detection accuracy over other existing D-MAD techniques.Comment: accepted in 5th International Conference on Bio-engineering for Smart Technologies (BIO-SMART 2023

    On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection

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    Face morphing attacks have raised critical concerns as they demonstrate a new vulnerability of Face Recognition Systems (FRS), which are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed image of high quality. The generated morphed image exhibits similar visual characteristics corresponding to the biometric characteristics of the data subjects that contributed to the composite image and thus making it difficult for both humans and FRS, to detect such attacks. In this paper, we report a systematic investigation on the vulnerability of the Commercial-Off-The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented. To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset. The dataset has two bins based on age intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects with the age variation of 1 year to 2 years while the MorphAge-II dataset consists of 516 data subjects whose age intervals are from 2 years to 5 years. To effectively evaluate the vulnerability for morphing attacks, we also introduce a new evaluation metric, namely the Fully Mated Morphed Presentation Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic scenario. Extensive experiments are carried out by using two different COTS FRS (COTS I - Cognitec and COTS II - Neurotechnology) to quantify the vulnerability with ageing. Further, we also evaluate five different Morph Attack Detection (MAD) techniques to benchmark their detection performance with ageing.Comment: Accepted in IJCB 202

    Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection

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    Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because it enables both individuals involved to exploit the same document. In this study, face embeddings serve two purposes: pre-selecting images for large-scale Morphing Attack generation and detecting potential Morphing Attacks. We build upon previous embedding studies in both use cases using the MagFace model. For the first objective, we employ an pre-selection algorithm that pairs individuals based on face embedding similarity. We quantify the attack potential of differently morphed face images to compare the usability of pre-selection in automatically generating numerous successful Morphing Attacks. Regarding the second objective, we compare embeddings from two state-of-the-art face recognition systems in terms of their ability to detect Morphing Attacks. Our findings demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Both open-source and COTS face recognition systems are susceptible to generated attacks, particularly when pre-selection is based on embeddings rather than random pairing which was only constrained by soft biometrics. More accurate face recognition systems exhibit greater vulnerability to attacks, with COTS systems being the most susceptible. Additionally, MagFace embeddings serve as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the advantages of face embeddings in more effective image pre-selection for face morphing and accurate detection of morphed face images. This is supported by extensive analysis of various designed attacks. The MagFace model proves to be a powerful alternative to the commonly used ArcFace model for both objectives, pre-selection and attack detection

    A Comprehensive Analysis of AI Biases in DeepFake Detection With Massively Annotated Databases

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    In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate the bias issue caused by public Deepfake datasets by (a) providing large-scale demographic and non-demographic attribute annotations of 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets. The investigation analyses the influence of a large variety of distinctive attributes (from over 65M labels) on the detection performance, including demographic (age, gender, ethnicity) and non-demographic (hair, skin, accessories, etc.) information. The results indicate that investigated databases lack diversity and, more importantly, show that the utilised Deepfake detection backbone models are strongly biased towards many investigated attributes. The Deepfake detection backbone methods, which are trained with biased datasets, might output incorrect detection results, thereby leading to generalisability, fairness, and security issues. We hope that the findings of this study and the annotation databases will help to evaluate and mitigate bias in future Deepfake detection techniques. The annotation datasets and the corresponding code are publicly available

    Effect of functionalization of polymeric nanoparticles incorporated with whole attenuated rabies virus antigen on sustained release and efficacy

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    Nanovaccines introduced a new dimension to prevent or cure diseases in an efficient and sustained manner. Various polymers have been used for the drug delivery to increase the therapeutic value with minimal side effects. Thus the present study incorporates both nanotechnology and polymers for the drug delivery. Poly(D,L-lactic-co-glycolic acid)-b-poly(ethylene glycol) was incorporated with the rabies whole attenuated viral antigen using double emulsion (W/O/W) method and characterized by Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). Chitosan-PEG nanoparticles incorporated with the rabies whole attenuated virus antigen (CS-PEG NP-RV Ag.) were prepared using Ionic Gelation method. The CS-PEG NP-RV Ag. was surface modified with biocompatible polymers such as Acacia, Bovine Serum Albumin (BSA), Casein, Ovalbumin and Starch by Ionic Gelation method. The morphology was confirmed by SEM and Transmission Electron Microscopy (TEM). The surface modification was confirmed by Fourier Transform Infrared Spectroscopy (FTIR), Zeta potential. The size distribution of CS-PEG-RV Ag. and surface modified CS-PEG-RV Ag. by respective biocompatible polymers was assessed by Zetasizer. Release profile of both stabilized nanoparticles was carried out by modified centrifugal ultrafiltration method which showed the sustained release pattern of the Rabies Ag. Immune stimulation under in-vitro condition was studied using rosette assay and phagocytosis assay. In-vitro toxicity using human blood and genotoxicity using human blood DNA was also studied to assess the toxicity of the nanoformulations. The results of these studies infer that PLGA-b-PEG nanoparticles, CS-PEG and surface modified CS-PEG nanoparticles may be an efficient nanocarrier for the RV Ag. to elicit immune response sustainably with negligible toxic effect to the human system
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