More Like this

1. Evaluation of Local Binary Pattern Algorithm for User Authentication with Face Biometric

   https://doi.org/10.1109/icmla51294.2020.00170  

   Gwyn, Tony ; Atay, Mustafa ; Kaushik, Roy ; Esterline, Albert ( December 2020 , 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   In the ever-changing world of computer security and user authentication, the username/password standard is becoming increasingly outdated. Using the same username and password across multiple accounts and websites leaves a user open to vulnerabilities, and the need to remember multiple usernames and passwords feels very unnecessary in the current digital age. Authentication methods of the future need to be reliable and fast, while maintaining the ability to provide secure access. Augmenting traditional username-password standard with face biometric is proposed in the literature to enhance the user authentication. However, this technique still needs an extensive evaluation study to show how reliable and effective it will be under different settings. Local Binary Pattern (LBP) is a discrete yet powerful texture classification scheme, which works particularly well with image classification for facial recognition. The system proposed here strives to examine and test various LBP configurations to determine their image classification accuracy. The most favorable configurations of LBP should be examined as a potential way to augment the current username and password standard by increasing their security with facial biometrics. 

   more » « less
2. SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

   https://doi.org/10.3390/fi14120354  

   Xu, Ronghua ; Chen, Yu ; Chen, Genshe ; Blasch, Erik ( December 2022 , Future Internet)

   The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution. 

   more » « less
3. On the Root of Trust Identification Problem

   https://doi.org/10.1145/3412382.3458274  

   De Oliveira Nunes, Ivan ; Ding, Xuhua ; Tsudik, Gene ( May 2021 , IPSN '21: Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021))

   null (Ed.)

   Trusted Execution Environments (TEEs) are becoming ubiquitous and are currently used in many security applications: from personal IoT gadgets to banking and databases. Prominent examples of such architectures are Intel SGX, ARM TrustZone, and Trusted Platform Modules (TPMs). A typical TEE relies on a dynamic Root of Trust (RoT) to provide security services such as code/data confidentiality and integrity, isolated secure software execution, remote attestation, and sensor auditing. Despite their usefulness, there is currently no secure means to determine whether a given security service or task is being performed by the particular RoT within a specific physical device. We refer to this as the Root of Trust Identification (RTI) problem and discuss how it inhibits security for applications such as sensing and actuation. We formalize the RTI problem and argue that security of RTI protocols is especially challenging due to local adversaries, cuckoo adversaries, and the combination thereof. To cope with this problem we propose a simple and effective protocol based on biometrics. Unlike biometric-based user authentication, our approach is not concerned with verifying user identity, and requires neither pre-enrollment nor persistent storage for biometric templates. Instead, it takes advantage of the difficulty of cloning a biometric in real-time to securely identify the RoT of a given physical device, by using the biometric as a challenge. Security of the proposed protocol is analyzed in the combined Local and Cuckoo adversarial model. Also, a prototype implementation is used to demonstrate the protocol’s feasibility and practicality. We further propose a Proxy RTI protocol, wherein a previously identified RoT assists a remote verifier in identifying new RoTs. 

   more » « less
4. Iris Print Attack Detection using Eye Movement Signals

   https://doi.org/10.1145/3517031.3532521  

   Raju, Mehedi Hasan ; Lohr, Dillon J ; Komogortsev, Oleg ( January 2022 , 2022 Symposium on Eye Tracking Research and Applications)

   Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infra-red light and an image sensor. This similarity potentially enables eye tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art. 

   more » « less
5. Face description using anisotropic gradient: thermal infrared to visible face recognition

   https://doi.org/10.1117/12.2304898  

   Wan, Qianwen ; Rao, Shishir Paramathma ; Panetta, Karen ; Agaian, Sos S. ; Kaszowska, Aleksandra ; Taylor, Holly ; Voronina, V. ( May 2018 , Mobile Multimedia/Image Processing, Security, and Applications 2018)

   Agaian, Sos S. ; Jassim, Sabah A. (Ed.)

   Face recognition technologies have been in high demand in the past few decades due to the increase in human-computer interactions. It is also one of the essential components in interpreting human emotions, intentions, facial expressions for smart environments. This non-intrusive biometric authentication system relies on identifying unique facial features and pairing alike structures for identification and recognition. Application areas of facial recognition systems include homeland and border security, identification for law enforcement, access control to secure networks, authentication for online banking and video surveillance. While it is easy for humans to recognize faces under varying illumination conditions, it is still a challenging task in computer vision. Non-uniform illumination and uncontrolled operating environments can impair the performance of visual-spectrum based recognition systems. To address these difficulties, a novel Anisotropic Gradient Facial Recognition (AGFR) system that is capable of autonomous thermal infrared to visible face recognition is proposed. The main contribution of this paper includes a framework for thermal/fused-thermal-visible to visible face recognition system and a novel human-visual-system inspired thermal-visible image fusion technique. Extensive computer simulations using CARL, IRIS, AT&T, Yale and Yale-B databases demonstrate the efficiency, accuracy, and robustness of the AGFR system. Keywords: Infrared thermal to visible facial recognition, anisotropic gradient, visible-to-visible face recognition, nonuniform illumination face recognition, thermal and visible face fusion method 

   more » « less