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Section: Publications & Resources
There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs) trained to discriminate speakers, pass-phrases, and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved.
Section: Publications & Resources
Automated biometric identification systems are inherently challenged to optimize false (non-)match rates. This can be addressed either by directly improving comparison subsystems, or indirectly by allowing only “good quality” biometric queries to be compared.
Section: Publications & Resources
Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML)-based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers.
Section: Publications & Resources
Current anomaly detection systems (ADSs) apply statistical and machine learning algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced persistent threat actors. In this paper, we propose an adversarial statistical learning mechanism for anomaly detection, outlier Dirichlet mixture-based ADS (ODM-ADS), which has three new capabilities.
Section: Publications & Resources
Low-level criminals, who do the legwork in a criminal organization, are the most likely to be arrested, whereas the high-level ones tend to avoid attention. But crippling the work of criminal organizations is not possible unless investigators can identify the most influential, high-level members and monitor their communication channels.
Section: Publications & Resources
Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks.
Section: Publications & Resources
Electric network frequency (ENF) is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) due to changes in the supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates.
Section: Publications & Resources
We introduce the multiple enrollment scheme for SRAM-physical unclonable functions (PUFs). During each enrollment, the binary power-on values of the SRAM are observed, and a corresponding key and helper data are generated. Each key can later be reconstructed from an additional observation and the helper data.
Section: Publications & Resources
In September 2017, the McAfee Labs quarterly report estimated that brute-force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser-based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems and in the design of better security protocols.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
Being able to cover a wide range of views, pan-tilt-zoom (PTZ) cameras have been widely deployed in visual surveillance systems. To achieve a global-view perception of a surveillance scene, it is necessary to generate its panoramic background image, which can be used for the subsequent applications such as road segmentation, active tracking, and so on.
