Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets

Security analysis of a fingerprint-secured USB drive

In response to user demands for mobile data security and maximum ease of use, fingerprint-secured mobile storage devices have been increasingly available for purchase. A fingerprint-secured Universal Serial Bus (USB) drive looks like a regular USB drive, except that it has an integrated optical scanner. When a fingerprint-secured USB drive is plugged into a computer running Windows, a program on this drive will run automatically to ask for fingerprint authentication. (When the program runs the very first time, it will ask for fingerprint enrollment). After a successful fingerprint authentication, a new private drive (for example, drive G:) will appear and data stored on the private drive can be accessed. This private drive will not appear if the fingerprint authentication fails. This thesis studies the security of a representative fingerprint-secured USB drive referred to by the pseudonym AliceDrive. Our results are two fold. First, through black-box reverse engineering and manipulation of binary code in a DLL, we bypassed AliceDrive’s fingerprint authentication and accessed the private drive without actually presenting a valid fingerprint. Our attack is a class attack in that the modified DLL can be distributed to any naive user to bypass AliceDevice’s fingerprint authentication. Second, in our security analysis of AliceDrive, we recovered fingerprint reference templates from memory, which may make AliceDrive worse than a regular USB drive: when Alice loses her fingerprint-secured USB drive, she does not only lose her data, she also loses her fingerprints, which are difficult to recover as Alice’s fingerprints do not change much over a long period of time. In this thesis, we also explore details in integrating fuzzy vault schemes to enhance the security of AliceDrive

Keystroke dynamics as signal for shallow syntactic parsing

Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural language processing models? We postulate that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing. To test this hypothesis, we explore labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising results on two shallow syntactic parsing tasks, chunking and CCG supertagging. Our model is simple, has the advantage that data can come from distinct sources, and produces models that are significantly better than models trained on the text annotations alone.Comment: In COLING 201