Deep face recognition: A survey
Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …
multiple levels of feature extraction. This emerging technique has reshaped the research …
A comprehensive overview of biometric fusion
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 …
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
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 …
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
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 …
the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at …
Biometrics: Trust, but verify
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 …
applications around the globe. This proliferation can be attributed to the high levels of …
Adversarial attacks on GMM i-vector based speaker verification systems
This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based
speaker verification systems to adversarial attacks, and the transferability of adversarial …
speaker verification systems to adversarial attacks, and the transferability of adversarial …
Benchmarking image classifiers for physical out-of-distribution examples detection
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 …
concerns about their robustness in the real world. Recent works in this field have well …
Detecting and mitigating adversarial perturbations for robust face recognition
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 …
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
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 …
However, it is also well established that they are highly vulnerable to adversarial …
On the robustness of face recognition algorithms against attacks and bias
Face recognition algorithms have demonstrated very high recognition performance,
suggesting suitability for real world applications. Despite the enhanced accuracies …
suggesting suitability for real world applications. Despite the enhanced accuracies …