Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks

Under the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has been advanced, to rapidly and accurately detect COVID-19 cases, because the need for expert radiologists, who are limited in number, forms a bottleneck for the screening. However, so far, the vulnerability of DNN-based systems has been poorly evaluated, although DNNs are vulnerable to a single perturbation, called universal adversarial perturbation (UAP), which can induce DNN failure in most classification tasks. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs generated using simple iterative algorithms. We consider nontargeted UAPs, which cause a task failure resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to nontargeted and targeted UAPs, even in case of small UAPs. In particular, 2% norm of the UPAs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the nontargeted and targeted attacks, respectively. Due to the nontargeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs make the DNN models classify most chest X-ray images into a given target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of the DNN models using UAPs improves the robustness of the DNN models against UAPs.Comment: 17 pages, 5 figures, 3 table

Computing weak distances between the 2-sphere and its nonsmooth approximations

A novel algorithm is proposed for quantitative comparisons between compact surfaces embedded in the three-dimensional Euclidian space. The key idea is to identify those objects with the associated surface measures and compute distances between them using the Fourier transform on the ambient space. In particular, the inhomogeneous Sobolev norms of negative order are approximated from data in the frequency space, which amounts to comparing measures after appropriate smoothing. Such Fourier-based distances allow several advantages including high accuracy due to fast-converging numerical quadrature rules, acceleration by the nonuniform fast Fourier transform, parallelization on massively parallel architectures. In numerical experiments, the 2-sphere, which is an example whose Fourier transform is explicitly known, is compared with its icosahedral discretization, and it is observed that the piecewise linear approximations converge to the smooth object at the quadratic rate up to small truncations.Comment: 14 pages, 4 figure

Theory of Non-Hermitian Fermionic Superfluidity on a Honeycomb Lattice: Interplay between Exceptional Manifolds and Van Hove Singularity

We study the non-Hermitian fermionic superfluidity subject to dissipation of Cooper pairs on a honeycomb lattice, for which we analyze the attractive Hubbard model with a complex-valued interaction. Remarkably, we demonstrate the emergence of the dissipation-induced superfluid phase that is anomalously enlarged by a cusp on the phase boundary. We find that this unconventional phase transition originates from the interplay between exceptional lines and van Hove singularity, which has no counterpart in equilibrium. Moreover, we demonstrate that the infinitesimal dissipation induces the nontrivial superfluid solution at the critical point. Our results can be tested in ultracold atoms with photoassociation techniques by postselcting special measurement outcomes with the use of quantum-gas microscopy and pave the way for understanding non-Hermitian many-body physics triggered by exceptional manifolds in open quantum systems.Comment: 7+5 pages, 4+3 figure

Augmented Reality Display of Robot with Graphs of Property Response Using Its USD Model

This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.22nd International Conference on Control, Automation and Systems ,ICCAS 2022, November 27 - December 1, 2022, Busan, Korea (Hybrid conference

Simple black-box universal adversarial attacks on deep neural networks for medical image classification

Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only input queries are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to white-box conditions in which adversaries can access model parameters. Nevertheless, we propose a method for generating UAPs using a simple hill-climbing search based only on DNN outputs to demonstrate that UAPs are easily generatable using a relatively small dataset under black-box conditions with representative DNN-based medical image classifications. Black-box UAPs can be used to conduct both nontargeted and targeted attacks. Overall, the black-box UAPs showed high attack success rates (40–90%). The vulnerability of the black-box UAPs was observed in several model architectures. The results indicate that adversaries can also generate UAPs through a simple procedure under the black-box condition to foil or control diagnostic medical imaging systems based on DNNs, and that UAPs are a more serious security threat

Application of Different Anastomotic Methods for a Patient with Crohn\u27n Disease : Long-term Endoscopic Appearances of Hand-sewn Versus Biofragmentable Anastomosis Ring Method

After resection for ileocecal or ileocolonic Crohn\u27s disease (CD), anastomotic recurrence is common, and roughly one half of the cases who undergo hand-sewn anastomoses require further surgery for suture line recurrence. The other anastomoses methods, stapled anastomoses, had been compared with that of patients having hand-sewn anastomoses. But the type of anastomosis, whether stapled or hend-sewn, did not affect the rates of symptomatic or operative recurrence. A compression anastomosis device consisting of a biofragmentable anastomosis ring (VALTRAC^[○!R]) is used with new anastomosis methods, and no fragments remain in the anastomosis unlike with other anastomotic materials. There have been few reports regarding the employment of VALTRAC^[○!R] methods for anastomoses of patients with CD. We reported a 30-year-old male with a 14-year history of CD. In 1991, he was referred to our hospital for surgery because of stenoses of the ileum and terminal ileum, and underwent ileocecal resection. Ileocolic anastomosis was performed with a hand-sewn method. In 1996, the patient was referred to our hospital again for surgery because of an ileoileal fistula and multiple stenoses in the ileum and the anastomosis. Resection of the previous anastomosis was performed. Next, ileocolic anastomosis was performed using a VALTRAC^[○!R] method. Comparisons of the long-term appearance of two different anastomoses (one hand-sewn and the other done by VALTRAC^[○!R] methods) of the same portion of the intestine in the patient were reported herein

Nanocellulose Paper Semiconductor with a 3D Network Structure and Its Nano-Micro-Macro Trans-Scale Design

Semiconducting nanomaterials with 3D network structures exhibit various fascinating properties such as electrical conduction, high permeability, and large surface areas, which are beneficial for adsorption, separation, and sensing applications. However, research on these materials is substantially restricted by the limited trans-scalability of their structural design and tunability of electrical conductivity. To overcome this challenge, a pyrolyzed cellulose nanofiber paper (CNP) semiconductor with a 3D network structure is proposed. Its nano-micro-macro trans-scale structural design is achieved by a combination of iodine-mediated morphology-retaining pyrolysis with spatially controlled drying of a cellulose nanofiber dispersion and paper-crafting techniques, such as microembossing, origami, and kirigami. The electrical conduction of this semiconductor is widely and systematically tuned, via the temperature-controlled progressive pyrolysis of CNP, from insulating (1012 ω cm) to quasimetallic (10-2 ω cm), which considerably exceeds that attained in other previously reported nanomaterials with 3D networks. The pyrolyzed CNP semiconductor provides not only the tailorable functionality for applications ranging from water-vapor-selective sensors to enzymatic biofuel cell electrodes but also the designability of macroscopic device configurations for stretchable and wearable applications. This study provides a pathway to realize structurally and functionally designable semiconducting nanomaterials and all-nanocellulose semiconducting technology for diverse electronics.Koga H., Nagashima K., Suematsu K., et al. Nanocellulose Paper Semiconductor with a 3D Network Structure and Its Nano-Micro-Macro Trans-Scale Design. ACS Nano, 16(6), 8630-8640, 2022. https://doi.org/10.1021/acsnano.1c10728