Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks

This paper presents an experiment designed to test the resilience of several user verification systems based on face recognition technology against simple identity spoofing methods, such as trying to gain access to the system by using mobile camera shots of the users, their ID cards, or social media photos of them that are available online. We also aim at identifying the compression threshold above which a photo can be used to gain access to the system. Four major user verification tools were tested: Keyemon and Luxand Blink on Windows and Android Face Unlock and FaceLock on Android. The results show all tested systems to be vulnerable to even very crude attacks, indicating that the technology is not ready yet for adoption in applications where security rather than user convenience is the main concern

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques

Designing a facial spoofing database for processed image attacks

Face recognition systems are used for user authentication in everyday applications such as logging into a laptop or smartphone without need to memorize a password. However, they are still vulnerable to spoofing attacks, as for example when an imposter gains access to a system by holding a printed photo of the rightful user in front of the camera. In this paper we are concerned with the design of face image databases for evaluating the performance of anti-spoofing algorithms against such attacks. We present a new database, supporting testing against an enhancement of the attack, where the imposter processes the stolen image before printing it. By testing a standard antispoofing algorithm on the new database we show a significant decrease in its performance and, as a simple remedy to this problem, we propose the inclusion of processed imposter images into the training set

Anomaly Detection with Transformers in Face Anti-spoofing

Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNet as the backbone for anomaly detection in face anti-spoofing, and compare the performance of various one-class classifiers at the end of the pipeline, such as one-class SVM, Isolation Forest, and decoders. Test results on the RA and SiW databases show the proposed approach to be competitive as an anomaly detection method for face anti-spoofing

Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images

Liveness tests are techniques employed by face recognition authentication systems, aiming at verifying that a live face rather than a photo is standing in front of the system camera. In this paper, we study the resilience of a standard liveness test under imposter photo attacks, under the additional assumption that the photos used in the attack may have been processed by common image processing operations such as sharpening, smoothing and corruption with salt and pepper noise. The results verify and quantify the claim that this type of liveness tests rely on the imposter photo images being less sharp than live face images

Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks

This paper presents an experiment designed to test the resilience of several user verification systems based on face recognition technology against simple identity spoofing methods, such as trying to gain access to the system by using mobile camera shots of the users, their ID cards, or social media photos of them that are available online. We also aim at identifying the compression threshold above which a photo can be used to gain access to the system. Four major user verification tools were tested: Keyemon and Luxand Blink on Windows and Android Face Unlock and FaceLock on Android. The results show all tested systems to be vulnerable to even very crude attacks, indicating that the technology is not ready yet for adoption in applications where security rather than user convenience is the main concern

FTIZZY CONTROLLERS FOR SINGLE POINTCONTROLLER-I (SPC-l) SYSTEMS

Advances in computer technology have introduced computers everywhere. One of the fields that the computers have entered is the field of process control and data acquisition systems. On the other hand, fuzzy control is emerging as an alternative to conventional control to control different systems. this paper is concerned with applying fuzzy control to a locally designed and manufactured process controller. This controller is designated by single point controller-1 (SPC-l)' It is basically a flexible and general-purpose, stand-alone, single-point controller. The CPU section of the SPC-1 is the AT89C5l general purpose microcontroller. The fuzzy control algorithms were imple-mented as programs to be executed by this microcontroller. These programs were written in C language and were translated to machine language by Keil8051 C compiler p Vision V5.1.

Resilience of luminance based liveness tests under attacks with processed imposter images

Liveness tests are techniques employed by face recognition authentication systems, aiming at verifying that a live face rather than a photo is standing in front of the system camera. In this paper, we study the resilience of a standard liveness test under imposter photo attacks, under the additional assumption that the photos used in the attack may have been processed by common image processing operations such as sharpening, smoothing and corruption with salt and pepper noise. The results verify and quantify the claim that this type of liveness tests rely on the imposter photo images being less sharp than live face images

Using theoretical ROC curves for analysing machine learning binary classifiers

Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions Ps and Pn of the response belonging to either class, probability distributions for the cost of each type of misclassification, and compute a performance score from the expected cost. In machine learning, classifier responses are obtained experimentally and performance scores are computed directly from them, without any assumptions on Ps and Pn. Here, we argue that the omitted step of estimating theoretical distributions for Ps and Pn can be useful. In a biometric security example, we fit beta distributions to the responses of two classifiers, one based on logistic regression and one on ANNs, and use them to establish a categorisation into a small number of classes with different extremal behaviours at the ends of the ROC curves