Vulnerabilities of Facial Recognition and Countermeasures

2022-2-8
Aktaş, Utku
Due to the developments in deep learning, the use of face recognition systems started to spread rapidly. Face recognition systems, which are used for purposes such as unlocking phones, entering our offices, or tracking citizens of states, also bring several problems. Attackers can find a weakness of the face recognition systems and avoid detection by facial recognition systems in various ways. Additionally attackers may carry out an impersonation attack which is the act of tricking the system by looking like an authorized person. In this research, basic building blocks of face recognition algorithms, face recognition vulnerabilities, how the attacks occur and what precautions can be taken are examined. It has been understood that it is difficult to reach a generalizable result because of a variety of facial recognition systems. In addition, attacks and countermeasures may differ according to the target system.

Suggestions

Face Recognition Based on Embedding Learning
Karaman, Kaan; Koc, Aykut; Alatan, Abdullah Aydın (2018-09-11)
Face recognition is a key task of computer vision research that has been employed in various security and surveillance applications. Recently, the importance of this task has risen with the improvements in the quality of sensors of cameras, as well as with the increasing coverage of camera networks setup everywhere in the cities. Moreover, biometry-based technologies have been developed for the last three decades and have been available on many devices such as the mobile phones. The goal is to identify peop...
Automated learning rate search using batch-level cross-validation
Kabakcı, Duygu; Akbaş, Emre; Department of Computer Engineering (2019)
Deep convolutional neural networks are being widely used in computer vision tasks, such as object recognition and detection, image segmentation and face recognition, with a variety of architectures. Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given a specific network model and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the tr...
Face recognition using Eigenfaces and neural networks
Akalın, Volkan; Severcan, Mete; Department of Electrical and Electronics Engineering (2003)
A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages; preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto ...
GAYE: A face recognition system
Kepenekci, B; Tek, FB; Cilingir, O; Sakarya, U; Akar, Gözde (2004-01-21)
In this paper, a new face recognition system, GAYE, is presented. GAYE is a fully automatic system that detects and recognizes faces in cluttered scenes. The input of the system is any digitized image/image sequence that includes face/faces. The basic building blocks of the system are face detection, feature extraction and feature comparison. Face detection is based on skin color segmentation. For feature extraction, a novel approach is proposed that depends on the Gabor wavelet transform of the face image....
Perceptual quality preserving adversarial attacks
Aksoy, Bilgin; Temizel, Alptekin; Department of Modeling and Simulation (2019)
Deep learning is used in various succesful computer vision applications such as image classification. Deep neural networks (DNN) especially convolutional neural networks have reached above human level accuracy rates for image classification tasks. While DNNs have solved the image classification task and enabled its use in many practical applications, recent research has unveiled some properties which could degrade their performance. Adversarial images are samples that are intentionally modified by adding no...
Citation Formats
U. Aktaş, “Vulnerabilities of Facial Recognition and Countermeasures,” M.S. - Master Of Science Without Thesis, Middle East Technical University, 2022.