Face recognition systems have been targeted by recent physical adversarial machine learning attacks, which attach or project visible patterns on adversaries' faces to trick backend FR models. While these attacks have demonstrated effectiveness in the literature, they often rely on visibly suspicious patterns, are susceptible to environmental noise, or exhibit limited success rates in practice. In this paper, we propose a novel physical adversarial attack against deep face recognition systems, namely Agile (Adversarial Glasses with Infrared LasEr). It generates adjustable, invisible laser perturbations and emits them into the camera CMOS to launch dodging and impersonation attacks against facial biometrics systems. To do so, we first theoretically model physical adversarial perturbations and convert them to the digital domain. The generated synthesized attack signals are utilized to guide real-world laser settings. Our experiments with real-world attackers and a benchmark face database show that Agile is highly effective in DoS, dodging, and impersonation attacks. More importantly, the candidate impersonation target and optimal attack settings identified by Agile's attack synthesis approach are highly consistent with real-world physical attack results. The grey-box and black-box evaluation against commercial FR models also confirms the effectiveness of the Agile attack.

Virtual Optical Networks (VONs) offer scientists the opportunity to test and validate Software Defined Networking (SDN) controllers without requiring access to expensive optical equipment. A key component in VON is the real-time optical layer emulation (OLE) engine that must estimate the OSNR at the virtual receiver of every virtual lightpath that is established in the VON. This study investigates two algorithms for training a two-hidden layer Neural Network (NN) whose aim is to estimate the OSNR penalties caused by a series of virtual Wavelength Selective Switches (WSS) that are traversed by a virtual lightpath before reaching its virtual receiver. The NN solution yields good accuracy and only requires a few microseconds of computation time — a two key requirements for achieving a meaningful.

In dynamic-traffic Wavelength Division Multiplexing (WDM) networks, it is desirable to guarantee each optical circuit a minimum Optical Signal-to-Noise Ratio (OSNR) throughout the circuit's entire lifetime. The objective of this paper is to increase such guaranteed OSNR, which in turn enables bandwidth-efficient modulation formats to be applied to the circuit signals. The guaranteed OSNR is improved by applying OSNR-driven Spectrum Assignment (OSA) constraints to each fiber link in the network, based on its forecast traffic volume (offered load). Applying OSA constraints to fiber links has two conflicting outcomes. On the one hand, circuit requests may experience higher blocking probabilities due to the lack of usable spectrum. On the other hand, the adverse effects of medium nonlinearity may be contained, thus yielding improved circuit OSNR. Two OSA constraints are described and their favorable impact on circuit OSNR (up to 2.75 dB gain) is estimated by using a Gaussian Noise (GN) model and an event-driven simulation of two WDM networks supporting dynamic circuits.

We perform theoretical analysis and systematic measurement of the degree-of-polarization and eye-closure penalty for optical signals with orthogonal polarizations. Both the theory and experiment show that the symmetry of the DOP is maintained for the orthogonal polarizations under both first and higher-order PMD, whereas the symmetry of eye-closure penalty is broken under second-order PMD. As a result, an orthogonal polarization pair can have large disparity of eye-closure penalty despite an identical degree-of-polarization. We also demonstrate a novel approach to estimate the maximum eye-closure penalty asymmetry with three orthogonal polarizations on the Poincare sphere.