Deep face recognition: A survey

M Wang, W Deng - Neurocomputing, 2021 - Elsevier
Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

A comprehensive overview of biometric fusion

M Singh, R Singh, A Ross - Information Fusion, 2019 - Elsevier
The performance of a biometric system that relies on a single biometric modality (eg,
fingerprints only) is often stymied by various factors such as poor data quality or limited …

Adversarial examples—Security threats to COVID-19 deep learning systems in medical IoT devices

A Rahman, MS Hossain, NA Alrajeh… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Medical IoT devices are rapidly becoming part of management ecosystems for pandemics
such as COVID-19. Existing research shows that deep learning (DL) algorithms have been …

Security and privacy in IoT using machine learning and blockchain: Threats and countermeasures

N Waheed, X He, M Ikram, M Usman… - ACM computing …, 2020 - dl.acm.org
Security and privacy of users have become significant concerns due to the involvement of
the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at …

Biometrics: Trust, but verify

AK Jain, D Deb, JJ Engelsma - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

Adversarial attacks on GMM i-vector based speaker verification systems

X Li, J Zhong, X Wu, J Yu, X Liu… - ICASSP 2020-2020 …, 2020 - ieeexplore.ieee.org
This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based
speaker verification systems to adversarial attacks, and the transferability of adversarial …

Benchmarking image classifiers for physical out-of-distribution examples detection

O Ojaswee, A Agarwal, N Ratha - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
The rising popularity of deep neural networks (DNNs) in computer vision has raised
concerns about their robustness in the real world. Recent works in this field have well …

Detecting and mitigating adversarial perturbations for robust face recognition

G Goswami, A Agarwal, N Ratha, R Singh… - International Journal of …, 2019 - Springer
Deep neural network (DNN) architecture based models have high expressive power and
learning capacity. However, they are essentially a black box method since it is not easy to …

Image transformation-based defense against adversarial perturbation on deep learning models

A Agarwal, R Singh, M Vatsa… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep learning algorithms provide state-of-the-art results on a multitude of applications.
However, it is also well established that they are highly vulnerable to adversarial …

On the robustness of face recognition algorithms against attacks and bias

R Singh, A Agarwal, M Singh, S Nagpal… - Proceedings of the AAAI …, 2020 - aaai.org
Face recognition algorithms have demonstrated very high recognition performance,
suggesting suitability for real world applications. Despite the enhanced accuracies …