Ruizhe Gu | Searcharxiv

arXiv2026

Nonlinear Maxwell's Equations as a Boundary Value Problem in Multilayer Nanophotonics

Calculating strong-field, broadband nonlinear phenomena in complex nanophotonic structures remains difficult because it requires simultaneous handling of non-perturbative dynamics, impulsive processes, and structural complexity. Here, we introduce a unified framework that combines the transfer matrix method with an iterative Green's function approach. The linear multilayer response is incorporated as boundary conditions into a nonlinear boundary value problem, which is then solved self-consistently, naturally accommodating broad spectra and arbitrary nonlinearity. The framework is validated with two examples in the transparent and resonant regimes, respectively. The first demonstrates an impulsive spectral modulation in second harmonic generation that is captured only by a full-structure broadband treatment. The second demonstrates the entire dynamical evolution of exciton-polaritons in a pump-probe experiment reproduced by a unified simulation. This framework directly links nanophotonic design with ultrafast nonlinear dynamics.

physics.optics↗

arXiv2020

Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks

Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrectly. In this paper, we propose a kind of novel countermeasures based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It shows that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks.

cs.CR↗