109 research outputs found
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Neural networks play an increasingly important role in the field of machine
learning and are included in many applications in society. Unfortunately,
neural networks suffer from adversarial samples generated to attack them.
However, most of the generation approaches either assume that the attacker has
full knowledge of the neural network model or are limited by the type of
attacked model. In this paper, we propose a new approach that generates a
black-box attack to neural networks based on the swarm evolutionary algorithm.
Benefiting from the improvements in the technology and theoretical
characteristics of evolutionary algorithms, our approach has the advantages of
effectiveness, black-box attack, generality, and randomness. Our experimental
results show that both the MNIST images and the CIFAR-10 images can be
perturbed to successful generate a black-box attack with 100\% probability on
average. In addition, the proposed attack, which is successful on distilled
neural networks with almost 100\% probability, is resistant to defensive
distillation. The experimental results also indicate that the robustness of the
artificial intelligence algorithm is related to the complexity of the model and
the data set. In addition, we find that the adversarial samples to some extent
reproduce the characteristics of the sample data learned by the neural network
model
Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images
Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness
Weighted-Sampling Audio Adversarial Example Attack
Recent studies have highlighted audio adversarial examples as a ubiquitous
threat to state-of-the-art automatic speech recognition systems. Thorough
studies on how to effectively generate adversarial examples are essential to
prevent potential attacks. Despite many research on this, the efficiency and
the robustness of existing works are not yet satisfactory. In this paper, we
propose~\textit{weighted-sampling audio adversarial examples}, focusing on the
numbers and the weights of distortion to reinforce the attack. Further, we
apply a denoising method in the loss function to make the adversarial attack
more imperceptible. Experiments show that our method is the first in the field
to generate audio adversarial examples with low noise and high audio robustness
at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd
Adversarial Samples on Android Malware Detection Systems for IoT Systems
Many IoT(Internet of Things) systems run Android systems or Android-like
systems. With the continuous development of machine learning algorithms, the
learning-based Android malware detection system for IoT devices has gradually
increased. However, these learning-based detection models are often vulnerable
to adversarial samples. An automated testing framework is needed to help these
learning-based malware detection systems for IoT devices perform security
analysis. The current methods of generating adversarial samples mostly require
training parameters of models and most of the methods are aimed at image data.
To solve this problem, we propose a \textbf{t}esting framework for
\textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection
systems(TLAMD) for IoT Devices. The key challenge is how to construct a
suitable fitness function to generate an effective adversarial sample without
affecting the features of the application. By introducing genetic algorithms
and some technical improvements, our test framework can generate adversarial
samples for the IoT Android Application with a success rate of nearly 100\% and
can perform black-box testing on the system
Superconductivity in a new layered cobalt oxychalcogenide NaCoSeO with a 3 triangular lattice
Unconventional superconductivity in bulk materials under ambient pressure is
extremely rare among the 3 transition-metal compounds outside the layered
cuprates and iron-based family. It is predominantly linked to highly
anisotropic electronic properties and quasi-two-dimensional (2D) Fermi
surfaces. To date, the only known example of the Co-based exotic superconductor
was the hydrated layered cobaltate, NaCoO yHO, and its
superconductivity is realized in the vicinity of a spin-1/2 Mott state.
However, the nature of the superconductivity in these materials is still an
active subject of debate, and therefore, finding new class of superconductors
will help unravel the mysteries of their unconventional superconductivity. Here
we report the discovery of unconventional superconductivity at 6.3 K in
our newly synthesized layered compound NaCoSeO, in
which the edge-shared CoSe octahedra form [CoSe] layers with a
perfect triangular lattice of Co ions. It is the first 3 transition-metal
oxychalcogenide superconductor with distinct structural and chemical
characteristics. Despite its relatively low , material exhibits
extremely high superconducting upper critical fields, , which
far exceeds the Pauli paramagnetic limit by a factor of 3 - 4. First-principles
calculations show that NaCoSeO is a rare example of
negative charge transfer superconductor. This new cobalt oxychalcogenide with a
geometrical frustration among Co spins, shows great potential as a highly
appealing candidate for the realization of high- and/or unconventional
superconductivity beyond the well-established Cu- and Fe-based superconductor
families, and opened a new field in physics and chemistry of low-dimensional
superconductors
Tunable even- and odd-denominator fractional quantum Hall states in trilayer graphene
The fractional quantum Hall (FQH) states are exotic quantum many-body phases
whose elementary charged excitations are neither bosons nor fermions but
anyons, obeying fractional braiding statistics. While most FQH states are
believed to have Abelian anyons, the Moore-Read type states with even
denominators, appearing at half filling of a Landau level (LL), are predicted
to possess non-Abelian excitations with appealing potentials in topological
quantum computation. These states, however, depend sensitively on the orbital
contents of the single-particle LL wavefunction and the mixing between
different LLs. Although they have been observed in a few materials, their
non-Abelian statistics still awaits experimental confirmation. Here we show
magnetotransport measurements on Bernal-stacked trilayer graphene (TLG), whose
unique multiband structure facilitates the interlaced LL mixing, which can be
controlled by external magnetic and displacement fields. We observe a series of
robust FQH states including even-denominator ones at filling factors
, , and . In addition, we are able to finetune the
LL mixing and crossings to drive quantum phase transitions of these
half-filling states and their neighboring odd-denominator ones, exhibiting a
related emerging and waning behavior. Our results establish TLG as a
controllable system for tuning the weights of LL orbitals and mixing strength,
and a fresh platform to seek for non-Abelian quasi-particles
Microbial Community Succession and Response to Environmental Variables During Cow Manure and Corn Straw Composting
In composting system, the composition of microbial communities is determined by the constant change in the physicochemical parameters. This study explored the dynamics of bacterial and fungal communities during cow manure and corn straw composting using high throughput sequencing technology. The relationships between physicochemical parameters and microbial community composition and abundance were also evaluated. The sequencing results revealed the major phyla included Proteobacteria, Bacteroidetes, Firmicutes, Chloroflexi and Actinobacteria, Ascomycota, and Basidiomycota. Linear discriminant analysis effect size (LEfSe) illustrated that Actinomycetales and Sordariomycetes were the indicators of bacteria and fungi in the maturation phase, respectively. Mantel test showed that NO3--N, NH4+-N, TN, C/N, temperature and moisture content significantly influenced bacterial community composition while only TN and moisture content had a significant effect on fungal community structure. Structural equation model (SEM) indicated that TN, NH4+-N, NO3--N and pH had a significant effect on fungal abundance while TN and temperature significantly affected bacterial abundance. Our finding increases the understanding of microbial community succession in cow manure and corn straw composting under natural conditions
On the optimal search problem: The case when the target distribution is unknown
We consider the problem of searching for an object in a set of N locations (or bins) {C1,...CN}. The probability of the object being in the location Ci is p(i). Also, the probability of locating the object in the bin within a specified time, given that it is in the bin, is given by a function called the detection function. This is typically specified by an exponential function. The intention is to allocate the available resources so as to maximize the probability of locating the object. This problem has applications in searching large databases and in developing various military and strategic policies. All of the research done in this area has assumed the knowledge of the {p(i)} - the target distribution. In this paper we consider the problem of obtaining error bounds and estimating the target distribution. To our knowledge these are the first available results in this area, and are particularly interesting because the target distribution, in itself, is unobservable
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