An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware variants.On the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based classifiers.Commercial antivirus systems,in particular,have been identified as vulnerable to such AEs.This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers.Our attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack research.By directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack studies.Being a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based classifiers.Also,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality verification.Through thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware detectors.Furthermore,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense model.Through several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation techniques.In conclusion,our defense model alleviates the limitation of the most promising defense method,adversarial training,which is only effective against the AEs that are included in the training classifiers.

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial perturbations.The remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to misclassifications.Moreover,image denoising compromises the classification accuracy of original examples.To address these challenges in AE defense through image denoising,this paper proposes a novel AE detection technique.The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network structures.The used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting algorithm.By analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected effectively.This technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by denoising.The proposed approach demonstrates excellent detection performance against mainstream AE attacks.Experimental results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

A new method of constructing adversarial examplesfor quantum variational circuits

A quantum variational circuit is a quantum machine learning model similar to a neural network.A crafted adversarial example can lead to incorrect results for the model.Using adversarial examples to train the model will greatly improve its robustness.The existing method is to use automatic differentials or finite difference to obtain a gradient and use it to construct adversarial examples.This paper proposes an innovative method for constructing adversarial examples of quantum variational circuits.In this method,the gradient can be obtained by measuring the expected value of a quantum bit respectively in a series quantum circuit.This method can be used to construct the adversarial examples for a quantum variational circuit classifier.The implementation results prove the effectiveness of the proposed method.Compared with the existing method,our method requires fewer resources and is more efficient.

Defending Adversarial Examples by a Clipped Residual U-Net Model

Deep learning-based systems have succeeded in many computer vision tasks.However,it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks.These attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classification to object detection.The adversarial examples are carefully designed by injecting a slight perturbation into the clean images.The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network Defense.We have experimentally proved that our approach is better than previous defensive techniques.Our proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial perturbation.The proposed defensive approach is based on residual and U-Net learning.Many experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionfield.We have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model.

Adversarial Examples Protect Your Privacy on Speech Enhancement System

Speech is easily leaked imperceptibly.When people use their phones,the personal voice assistant is constantly listening and waiting to be activated.Private content in speech may be maliciously extracted through automatic speech recognition(ASR)technology by some applications on phone devices.To guarantee that the recognized speech content is accurate,speech enhancement technology is used to denoise the input speech.Speech enhancement technology has developed rapidly along with deep neural networks(DNNs),but adversarial examples can cause DNNs to fail.Considering that the vulnerability of DNN can be used to protect the privacy in speech.In this work,we propose an adversarial method to degrade speech enhancement systems,which can prevent the malicious extraction of private information in speech.Experimental results show that the generated enhanced adversarial examples can be removed most content of the target speech or replaced with target speech content by speech enhancement.The word error rate(WER)between the enhanced original example and enhanced adversarial example recognition result can reach 89.0%.WER of target attack between enhanced adversarial example and target example is low at 33.75%.The adversarial perturbation in the adversarial example can bring much more change than itself.The rate of difference between two enhanced examples and adversarial perturbation can reach more than 1.4430.Meanwhile,the transferability between different speech enhancement models is also investigated.The low transferability of the method can be used to ensure the content in the adversarial example is not damaged,the useful information can be extracted by the friendly ASR.This work can prevent the malicious extraction of speech.

Eye Movement Modeling Examples as a Teaching Tool for Epidural Block

Introduction: Video examples with task demonstrations by experts, with the expert’s eye movements superimposed on the task, are known as “eye movement modeling examples” (EMME). We performed this study to evaluate if there were improvements in the performance of anesthesia novice trainees when executing the epidural technique after an EMME of epidural block procedure. Methods: We developed an eye movement modeling example (EMME) from eye tracking recordings made by experienced anesthesiologists with more than 20 years of experience. Forty-two PGY3 anesthesia trainees who had never previously performed an epidural block were randomized to receive (study group) or not receive (control group) the EMME video before their institutional training. All the trainees were evaluated every 10 epidural blocks until the end of the rotation period, by an independent, blinded observer using the Global Rating Scale for Epidural Anesthesia (GRS). Results: Trainees who received the EMME training exhibited more respect for the patient’s tissues (P Discussion: This is the first study that has used the EMME for a practical, clinical teaching purpose on real patients and that has used it as an aid in teaching epidural anesthesia. We demonstrated that inexperienced trainees who received the EMME training improved their proficiency at epidural blocks as compared to those who had no EMME training beforehand. Given this result, we welcome further studies to investigate the impact and the role of EMME on clinical teaching in the field of anesthesia.

微探仁爱版初中英语教材中“Example”部分的教学策略

仁爱版初中英语教材中的“Example”部分是听说课教学的重要内容之一,也是学生在语言实践中迁移和运用知识的集中体现。如何提高“Example”部分教学内容的指导性、针对性和有效性是一线教师关注的焦点。文章分析了“Example”部分内容教学存在的问题,提出以主题意义为导向、深度融合教学与评价、合理分配教学时间和巩固教学成果等教学策略,以期提升“Example”部分的教学成效,逐步培养学生的英语学科核心素养。

Auto-expanded multi query examples technology in content-based image retrieval

In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.

Patterns of Clay Minerals Transformation in Clay Gouge, with Examples from Revers Fault Rocks in Devonina Niqiuhe Formation in The Dayangshu Basin

The role of authigenic clay growth in clay gouge is increasingly recognized as a key to understanding the mechanics of berittle faulting and fault zone processes,including creep and seismogenesis,and providing new insights into the ongoing debate about the frictional strength of brittle fault(Haines and van der Pluijm,2012).However,neither the conditions nor the processes which

A Survey on Adversarial Examples in Deep Learning

Adversarial examples are hot topics in the field of security in deep learning.The feature,generation methods,attack and defense methods of the adversarial examples are focuses of the current research on adversarial examples.This article explains the key technologies and theories of adversarial examples from the concept of adversarial examples,the occurrences of the adversarial examples,the attacking methods of adversarial examples.This article lists the possible reasons for the adversarial examples.This article also analyzes several typical generation methods of adversarial examples in detail:Limited-memory BFGS(L-BFGS),Fast Gradient Sign Method(FGSM),Basic Iterative Method(BIM),Iterative Least-likely Class Method(LLC),etc.Furthermore,in the perspective of the attack methods and reasons of the adversarial examples,the main defense techniques for the adversarial examples are listed:preprocessing,regularization and adversarial training method,distillation method,etc.,which application scenarios and deficiencies of different defense measures are pointed out.This article further discusses the application of adversarial examples which currently is mainly used in adversarial evaluation and adversarial training.Finally,the overall research direction of the adversarial examples is prospected to completely solve the adversarial attack problem.There are still a lot of practical and theoretical problems that need to be solved.Finding out the characteristics of the adversarial examples,giving a mathematical description of its practical application prospects,exploring the universal method of adversarial example generation and the generation mechanism of the adversarial examples are the main research directions of the adversarial examples in the future.

SOME EXAMPLES OF TRANSITION SPANS MINERALIZING AND ANALYSIS OFTHEIR DYNAMICS

The transitional span is a special environment for deposits. Taking peat, oil gas, metallic deposits as examples, this paper discusses the spatial temporal transitional characteristics of mineralization in transitional regions, points out the importance of the mineralization in transition spans, and analyses their dynamics finally.

The algorithm AE_(11) of learning from examples

We first put forward the idea of a positive extension matrix (PEM) on paper. Then, an algorithm, AE_ 11, was built with the aid of the PEM. Finally, we made the comparisons of our experimental results and the final result was fairly satisfying.

Examples for Clinical Use of Ma Zi Ren Wan

Ma Zi Ren Wan (麻子仁丸), originally recorded in Treatise on Febrile Diseases (伤寒论), is composed of Ma Zi Ren (麻子仁Fructus Cannabis), Bai Shao (白芍Radix Paeoniae Alba), Zhi Shi (枳实Fructus Aurantii Immaturus), Da Huang (大黄Radix etRhizoma Rhei), Hou Po (厚朴cortex Magnoliae Officinalis) and Xing Ren (杏仁Semen Armeniacae Amarum). Good therapeutic results have been achieved by using Ma ZiRen Wan in treatment of febrile disease at the restoring stage, chronic consumptive diseases, hemorrhoid, disorders in women after delivery, chronic kidney disease, senile constipation, pulmonary heart disease, diabetes, coronary heart disease and hypertension. Some illustrative cases are introduced below. ……

Environmental Implication of Subaqueous Lava Flows from A Continental Large Igneous Province: Examples from the Moroccan Central Atlantic Magmatic Province(CAMP)

The Early Jurassic volcanic sequence of the Central Atlantic Magmatic Province(CAMP)of Morocco is classically subdivided into four stratigraphic units:the Lower,Middle,Upper and Recurrent Formations separated

Performance driven expression mapping based on segmented examples

We present a method that combines performance-driven method with segmented 3D blendshape models to animate a face. First we prepare key sample examples and corresponding key target examples. Next we segment the whole face into two regions, for each region we reduce dimensionality of source examples using PAC into abstract space which is defined by truncated PCA eigen- vectors. Then for each example we fix the cardinal base function, which can determine the weight of the target example. Finally, in the animation stage we compute the weight of each example for each frame and add the weighted displacement vectors of each re- gion on the general face model.

从学习进阶到内容重构:基于情境链的体育与健康课程大单元的内部构建

情境链是体育与健康课程大单元教学内容重构的重要支撑,能有效解决中小学体育教学实践中的教学内容低级重复、情境创设连贯性较弱和延展性不够等问题。基于情境链的体育与健康课程大单元教学重视链式整合的课程样态,依托递进的情境进行专项运动技能的有效学练,通过情境链串联目标、任务、内容等课程元素,整体规划体育与健康课程大单元的学习路径。情境认知理论、境脉学习理论与学习进阶理论为情境链的深入理解提供了理论基石与实操框架。结合三大理论的基本要素探讨基于情境链的体育与健康课程大单元的构建流程,聚焦核心素养的整体性,通过水平二“篮球保护和控制球能力的提升”大单元主题案例,从“精准分析学情,确立大单元学习目标”“提取关键概念,规划大单元学习路径”“串联进阶元素,重构大单元学习内容”“基于真实情境,评价学生的学习表现”展现完整的情境链研究。

中国画元素在鞋服设计中的融合与应用

中国画坚持笔墨传神、色彩统一,是我国传统艺术中内容与形式的融合的重要体现。中国画元素的特征受到众多设计行业的青睐。本文在介绍中国画元素的基础上,立足鞋服产业,分析中国画的色彩、图案及构图方法在鞋服设计中的应用,借助具体的案例加以说明,并对中国画元素在鞋服设计中的未来趋势进行了展望,希望能够为鞋服设计者提供新的设计灵感。

结合对抗训练和特征混合的孪生网络防御模型

神经网络模型容易受到对抗样本攻击。针对当前防御方法侧重改进模型结构或模型仅使用对抗训练方法导致防御类型单一且损害模型分类能力、效率低下的问题,提出结合对抗训练和特征混合训练孪生神经网络模型(SS-ResNet18)的方法。该方法通过线性插值混合训练集样本数据,使用残差注意力模块搭建孪生网络模型,将PGD对抗样本和正常样本输入不同分支网络进行训练。在特征空间互换相邻样本部分输入特征以增强网络抗干扰能力,结合对抗损失和分类损失作为网络整体损失函数并对其进行标签平滑。在CIFAR-10和SVHN数据集上进行实验,该方法在白盒攻击下表现出优异的防御性能,黑盒攻击下模型对PGD、JSMA等对抗样本的防御成功率均在80%以上;同时,SS-ResNet18模型时间花销仅为子空间对抗训练方法的二分之一。实验结果表明,SS-ResNet18模型能防御多种对抗样本攻击,与现有防御方法相比,其鲁棒性强且训练耗时较短。

论思想的客观性

语言表达认识。语言所表达的东西,即思想,具有客观性。语言在表达思想的时候还带有一种东西,这就是真之条件,即思想在什么情况下是真的。后者也是客观的。人们断定一个句子所表达的东西是不是真的,需要两个条件:其一,思想必须是人们认识的,即在人们经验范围之内;其二,句子的真之条件必须是清楚的,否则人们就无法判定思想是不是真的。对思想的认识是经验的,但是对真之条件的认识不是经验的,而需要借助逻辑的理论和方法。人们一般不知道真之条件,这通常并不影响人们进行交流。哲学中的举例说明就是借助对思想的理解。今天,人们借助关于真之条件的认识,对句子及其表达的东西作出更好的说明。哲学史的发展表明,人们从借助关于语言的考虑进步到借助逻辑的理论和方法,从关于真及其相关问题的探讨进步到关于真之条件的认识,从而使真与意义真正结合起来。