Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Secure Face and Liveness Detection with Criminal Identification for Security Systems

The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security. The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely

Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection.

Identity theft has had a detrimental impact on the reliability of face recognition, which has been extensively employed in security applications. The most prevalent are presentation attacks. By using a photo, video, or mask of an authorized user, attackers can bypass face recognition systems. Fake presentation attacks are detected by the camera sensors of face recognition systems using face presentation attack detection. Presentation attacks can be detected using convolutional neural networks, commonly used in computer vision applications. An in-depth analysis of current deep learning methods is used in this research to examine various aspects of detecting face presentation attacks. A number of new techniques are implemented and evaluated in this study, including pre-trained models, manual feature extraction, and data aggregation. The thesis explores the effectiveness of various machine learning and deep learning models in improving detection performance by using publicly available datasets with different dataset partitions than those specified in the official dataset protocol. Furthermore, the research investigates how deep models and data aggregation can be used to detect face presentation attacks, as well as a novel approach that combines manual features with deep features in order to improve detection accuracy. Moreover, task-specific features are also extracted using pre-trained deep models to enhance the performance of detection and generalisation further. This problem is motivated by the need to achieve generalization against new and rapidly evolving attack variants. It is possible to extract identifiable features from presentation attack variants in order to detect them. However, new methods are needed to deal with emerging attacks and improve the generalization capability. This thesis examines the necessary measures to detect face presentation attacks in a more robust and generalised manner

Presentation Attack Detection in Facial Biometric Authentication

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in detecting liveness. The Convolutional Neural Network (CNN) model trained on the dataset gave 95% accuracy. In the case of deepfake attacks, it is found out that the deepfake videos and photos are generated by complex Generative Adversarial Networks (GANs) and are difficult for human eye to figure out. However, through experiments, it was observed that comprehensive analysis on the frequency domain divulges a lot of vulnerabilities in the GAN generated images. This makes it easier to separate these fake face images from real live faces. The project documents that with frequency analysis, simple linear models as well as complex models give high accuracy results. The models are trained on StyleGAN generated fake images, Flickr-Faces-HQ Dataset and Reface app generated video dataset. Logistic Regression turns out to be the best classifier with test accuracies of 99.67% and 97.96% on two different datasets. Future research can be conducted on different types of presentation attacks like using video, 3-D rendered face mask or advanced GAN generated deepfakes