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WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

WBA: A Warping-based Approach to Generating Imperceptible Ad...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chengyao Hua Shigeng Zhang Weiping Wang Zhankai Li Jian Zhang School of Computer Science and Engineering Central South University Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL)

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and imageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.

关键词： Measurement Deep learning Privacy image recognition Perturbation methods Neural networks Robustness

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Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing 4

Task Distribution Method for Index Classification based on C...

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4th International conference on Advanced Information Technologies, ICAIT 2020

作者： Htun, Yan Naing Aye, Bawin Aung, Aung Higher Education Center Department of Computer Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9781728183640

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-Tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer. © 2020 IEEE.

关键词： image enhancement

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DOS-GAN: A distributed Over-Sampling Method Based on Generative Adversarial Networks for distributed Class-Imbalance Learning 20th

DOS-GAN: A Distributed Over-Sampling Method Based on Generat...

引用

20th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Guan, Hongtao Ma, Xingkong Shen, Siqi Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783030602482;9783030602475

Class-imbalance Learning is one of the hot research issues in machine learning. In the practical application of distributed class-imbalance learning, data continues to arrive, which often leads to classimbalance situations. The imbalance problem in the distributed scenario is particular: the imbalanced state of different nodes may be complementary. The imbalanced states of different nodes may be complementary. Using this complementary relationship to do oversampling to change the imbalanced state is a valuable method. However, the data island limits data sharing in this case between the nodes. To this end, we propose DOS-GAN, which can take turns to use the data of one same class data on multiple nodes to train the global GAN model, and then use this GAN generator to oversampling the class without the original data being exchanged. Extensive experiments confirm that DOS-GAN outperforms the combination of traditional methods and achieves classification accuracy closes to the method of data aggregating.

关键词： Class-imbalance learning distributed learning Generative adversarial networks Data isolated islands

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A parallel Drone image Mosaic Method Based on Apache Spark 9th

A Parallel Drone Image Mosaic Method Based on Apache Spark

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Wu, Yirui Ge, Lanbo Luo, Yuchi Teng, Deqiang Feng, Jun College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9783030485122

MapReduce has been widely used to process large-scale data in the past decade. Among the quantity of such cloud computing applications, we pay special attention to distributed mosaic methods based on numerous drone images, which suffers from costly processing time. In this paper, a novel computing framework called Apache Spark is introduced to pursue instant responses for the quantity of drone image mosaic requests. To assure high performance of Spark-based algorithms in a complex cloud computing environment, we specially design a distributed and parallel drone image mosaic method. By modifying to be fit for fast and parallel running, all steps of the proposed mosaic method can be executed in an efficient and parallel manner. We implement the proposed method on Apache Spark platform and apply it to a few self-collected datasets. Experiments indicate that our Spark-based parallel algorithm is of great efficiency and is robust to process low-quality drone aerial images. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Drones

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An Unsupervised Approach For Detecting Lateral Movement Logins Based On Knowledge Graph

An Unsupervised Approach For Detecting Lateral Movement Logi...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Xiaohan Ma Chen Li Bibo Tu Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The lateral movement that relies on privacy theft is receiving increasing attention. This type of attack can be achieved by Cross-Host Login. Constructing an authentication graph between hosts is a common way to detect lateral movement, but the previous graph-based detection methods lack an effective means to integrate the account information into graph-based features. We abstract the login events as edges on the knowledge graph and account as the relations on the edges. Then we utilize a graph auto-encoder model comprised of an entity encoder and a scoring function (decoder), where entities are represented as low-dimensional vectors learned from the relational graph neural network. The decoder scores the edges to get the likelihood of the edges belonging to normal behaviors. We also propose a novel negative sample approach to reduce the false alarm rate. We apply this technique to authentication data derived from a large-scale real-world environment. The results show our approach can detect malicious authentication events associated with lateral movement with a true positive rate of 95 % and false positive rate of 10%, compared to 85% and 0.9% by the previous random walk approach as a baseline.

关键词： Privacy image edge detection Authentication Knowledge graphs Feature extraction Resource description framework Graph neural networks

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Unified Density-Aware image Dehazing and Object Detection in Real-World Hazy Scenes 15th

Unified Density-Aware Image Dehazing and Object Detection in...

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15th Asian conference on Computer Vision, ACCV 2020

作者： Zhang, Zhengxi Zhao, Liang Liu, Yunan Zhang, Shanshan Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High -Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783030695378

It is an important yet challenging task to detect objects on hazy images in real-world applications. The major challenge comes from low visual quality and large haze density variations. In this work, we aim to jointly solve the image dehazing and the object detection tasks in real hazy scenarios by using haze density as prior knowledge. Our proposed Unified Dehazing and Detection (UDnD) framework consists of three parts: a residual-aware haze density classifier, a density-aware dehazing network, and a density-aware object detector. First, the classifier exploits the residuals of hazy images to accurately predict density levels, which provide rich domain knowledge for the subsequent two tasks. Then, we design respectively a High-Resolution Dehazing Network (HRDN) and a Faster R-CNN-based multi-domain object detector to leverage the extracted density information and tackle hazy object detection. Experiments demonstrate that UDnD performs favorably against other methods for object detection in real-world hazy scenes. Also, HRDN achieves better results than state-of-the-art dehazing methods in terms of PSNR and SSIM. Hence, HRDN can conduct haze removal effectively, based on which UDnD is able to provide high-quality detection results. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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The research of implementation method of canny edge detection of video on FPGA

The research of implementation method of canny edge detectio...

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2020 International conference on Engineering Management of Communication and Technology, EMCTECH 2020

作者： Tan, Li Quan He, Bin School of Information Science and Technology Beijing University of Chemical Technology 100029 China

ISBN: (纸本)9781665404488

Edge detection algorithm has an important application in video image processing. The algorithm overcomes the shortcomings of traditional video image edge detection methods, and significantly improves the quality of video image edge detection. However, the amount of calculation is very large, and the traditional implementation method is difficult to meet the real-time requirements of video image edge detection, and the implementation cost is high. In this design, field programmable gate array (FPGA) is used as the core to construct and implement Canny video image edge detection system. Firstly, the video image is collected by ov7725 camera in real time;then, the edge detection of video image based on Canny algorithm is implemented in FPGA. Finally, the effect of Canny video image edge detection is compared and analyzed. This design has a good reference value for canny video image edge detection. © 2020 IEEE.

关键词： image enhancement

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Recommending the Most Confusing images to the Annotators via Confusion Graph for the Large-Scale Face Dataset Annotation 11

Recommending the Most Confusing Images to the Annotators via...

引用

11th International conference on Graphics and image processing (ICGIP)

作者： Zhao, Lei Qiao, Peng Dou, Yong Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha 410073 Peoples R China

ISBN: (纸本)9781510635241

image set annotation is an important task in the supervised training of the deep neural network. Manual and data-driven dataset annotation methods are commonly used approaches. Both of them have shortcomings, especially in the case of the dataset requiring professional knowledge, which leads to high cost with manual annotation methods and poorly diversified annotation samples with data-driven annotation methods. Although the recommendation annotation method based on cosine similarity using deep neural network features takes advantages of manual annotation and data-driven method, there are still problems such as low accuracy and click-through rate. In order to improve the recommendation accuracy and click-through rate, we propose a confusion graph recommendation annotation method, which builds a confusion graph based on the Largest Margin Nearest Neighbor (LMNN) distance among deep neural network features, to recommend the most confusing images to annotators. In this paper, we made ablation studies on the self-built child face dataset in terms of Precision, mAP (mean Average Precision), and CTR (click-through-rate). The experimental results show that the proposed method achieves superior performance, compared with the cosine similarity recommendation annotation method and the manual annotation method.

关键词： Machine-assisted manual annotating confusion graph largest margin nearest neighbor distance deep learning

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Zero-touch security automation mechanisms for edge NFV: the π-Edge approach

Zero-touch security automation mechanisms for edge NFV: the ...

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International conference on Network and Service Management

作者： Alexandros Valantasis Nikos Psaromanolakis Vasileios Theodorou Intracom Telecom Athens Greece

ISBN: (数字)9783903176515

ISBN: (纸本)9781665489928

The shift towards distributed computing architectures that push data storage and processing to the edge of the network, is resulting into a convergence of cloud-computing services and next generation mobile network technologies. In order to uniformly manage resources and services in the formed cloud/core to edge/devices continuum and to handle the diversity of multi-party underlying infrastructure technologies in a latency-aware, reliable and trustworthy fashion, management automation has become more crucial than ever. In this work, we present the security analytics mechanisms of the π-Edge platform–our edge management platform that embodies zero-touch automation features for interoperability, Quality of Service (QoS) assurance, resilience and trust. To this end, we introduce a declarative NFV MANO Information Model (IM) and methods for automatically enhancing Network Slices at the edge, with security services that i) continuously monitor user-plane traffic on the links between Virtual Network Functions (VNFs), ii) detect possible network vulnerabilities or malicious behaviour and iii) apply relevant actions to effectively observe and mitigate identified threats. The implementation of such mechanisms is evaluated through experimentation on a use case of DDoS attacking scenarios, showcasing the usability and the benefits of our proposed solution.

关键词： Cloud computing Automation image edge detection Quality of service Time measurement Security Resource management

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image Distillation Based Screening for X-ray Crystallography Diffraction images

Image Distillation Based Screening for X-ray Crystallography...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Lingyao Chen Kang Xu Xiaoying Zheng Yongxin Zhu Yi Jing Shanghai Information Technology Research Center Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

The modern synchrotron radiation facilities are producing massive diffraction images, which present a severe problem for data processing due to the high dimensionality of imaging data. Feature recognition and selection based deep learning methods have been developed to analyze data automatically. One crucial step is to use AI to screen out the diffraction images without Bragg spots. This paper proposes a feature distillation based approach for screening. It helps to reduce over 40% raw data volume and greatly alleviates the post processing workload faced by scientists.

关键词： Heating systems Deep learning Diffraction Imaging X-ray diffraction Data processing Crystallography

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