scholarly journals AN ASSESSMENT OF PRESENTATION ATTACK DETECTION METHODS FOR FACE RECOGNITION SYSTEMS

2018 ◽  
Author(s):  
GUILLERMO ESTRADA DOMECH
Keyword(s):  
Face Recognition ◽  
Attack Detection ◽  
Detection Methods ◽  
Recognition Systems ◽  
Presentation Attack Detection

Presentation Attack Detection Methods for Face Recognition Systems

2017 ◽  
Vol 50 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Raghavendra Ramachandra ◽  
Christoph Busch
Keyword(s):  
Face Recognition ◽  
Attack Detection ◽  
Detection Methods ◽  
Recognition Systems ◽  
Presentation Attack Detection

scholarly journals A Study on Presentation Attack Detection in Thermal Infrared

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3988
Author(s):  
Marcin Kowalski
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Infrared Imaging ◽  
Thermal Infrared ◽  
Attack Detection ◽  
Thermal Infrared Imaging ◽  
3D Printed ◽  
Recognition Systems ◽  
Thermal Face ◽  
Presentation Attack Detection

Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

scholarly journals MagNet: Detecting Digital Presentation Attacks on Face Recognition

2021 ◽  
Vol 4 ◽  
Author(s):  
Akshay Agarwal ◽  
Richa Singh ◽  
Mayank Vatsa ◽  
Afzel Noore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Error Rates ◽  
Attack Detection ◽  
Limited Attention ◽  
Feature Descriptor ◽  
Security Risk ◽  
Learning Networks ◽  
Social Media Platforms ◽  
Recognition Systems

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as IDAgender, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.

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