DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution

Recent research has demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks based on MasterPrints. MasterPrints are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints thereby undermining the security afforded by fingerprint systems. Previous work by Roy et al. generated synthetic MasterPrints at the feature-level. In this work we generate complete image-level MasterPrints known as DeepMasterPrints, whose attack accuracy is found to be much superior than that of previous methods. The proposed method, referred to as Latent Variable Evolution, is based on training a Generative Adversarial Network on a set of real fingerprint images. Stochastic search in the form of the Covariance Matrix Adaptation Evolution Strategy is then used to search for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer. Experiments convey the efficacy of the proposed method in generating DeepMasterPrints. The underlying method is likely to have broad applications in fingerprint security as well as fingerprint synthesis.Comment: 8 pages; added new verification systems and diagrams. Accepted to conference Biometrics: Theory, Applications, and Systems 201

Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks

Primary user emulation (PUE) attacks are an emerging threat to cognitive radio (CR) networks in which malicious users imitate the primary users (PUs) signals to limit the access of secondary users (SUs). Ascertaining the identity of the devices is a key technical challenge that must be overcome to thwart the threat of PUE attacks. Typically, detection of PUE attacks is done by inspecting the signals coming from all the devices in the system, and then using these signals to form unique fingerprints for each device. Current detection and fingerprinting approaches require certain conditions to hold in order to effectively detect attackers. Such conditions include the need for a sufficient amount of fingerprint data for users or the existence of both the attacker and the victim PU within the same time frame. These conditions are necessary because current methods lack the ability to learn the behavior of both SUs and PUs with time. In this paper, a novel transfer learning (TL) approach is proposed, in which abstract knowledge about PUs and SUs is transferred from past time frames to improve the detection process at future time frames. The proposed approach extracts a high level representation for the environment at every time frame. This high level information is accumulated to form an abstract knowledge database. The CR system then utilizes this database to accurately detect PUE attacks even if an insufficient amount of fingerprint data is available at the current time frame. The dynamic structure of the proposed approach uses the final detection decisions to update the abstract knowledge database for future runs. Simulation results show that the proposed method can improve the performance with an average of 3.5% for only 10% relevant information between the past knowledge and the current environment signals.Comment: 6 pages, 3 figures, in Proceedings of IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong Kong, P.R. China, Aug. 201

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201