Visible to the public Biblio

Filters: Keyword is Image edge detection  [Clear All Filters]
2021-07-27
Jiao, Rui, Zhang, Lan, Li, Anran.  2020.  IEye: Personalized Image Privacy Detection. 2020 6th International Conference on Big Data Computing and Communications (BIGCOM). :91–95.
Massive images are being shared via a variety of ways, such as social networking. The rich content of images raise a serious concern for privacy. A great number of efforts have been devoted to designing mechanisms for privacy protection based on the assumption that the privacy is well defined. However, in practice, given a collection of images it is usually nontrivial to decide which parts of images should be protected, since the sensitivity of objects is context-dependent and user-dependent. To meet personalized privacy requirements of different users, we propose a system IEye to automatically detect private parts of images based on both common knowledge and personal knowledge. Specifically, for each user's images, multi-layered semantic graphs are constructed as feature representations of his/her images and a rule set is learned from those graphs, which describes his/her personalized privacy. In addition, an optimization algorithm is proposed to protect the user's privacy as well as minimize the loss of utility. We conduct experiments on two datasets, the results verify the effectiveness of our design to detect and protect personalized image privacy.
2021-06-28
Lehrfeld, Michael R..  2020.  Preventing the Insider – Blocking USB Write Capabilities to Prevent IP Theft. 2020 SoutheastCon. 2:1–7.
The Edward Snowden data breach of 2013 clearly illustrates the damage that insiders can do to an organization. An insider's knowledge of an organization allows them legitimate access to the systems where valuable information is stored. Because they belong within an organizations security perimeter, an insider is inherently difficult to detect and prevent information leakage. To counter this, proactive measures must be deployed to limit the ability of an insider to steal information. Email monitoring at the edge is can easily be monitored for large file exaltation. However, USB drives are ideally suited for large-scale file extraction in a covert manner. This work discusses a process for disabling write-access to USB drives while allowing read-access. Allowing read-access for USB drives allows an organization to adapt to the changing security posture of the organization. People can still bring USB devices into the organization and read data from them, but exfiltration is more difficult.
2021-06-01
Xing, Hang, Zhou, Chunjie, Ye, Xinhao, Zhu, Meipan.  2020.  An Edge-Cloud Synergy Integrated Security Decision-Making Method for Industrial Cyber-Physical Systems. 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). :989–995.
With the introduction of new technologies such as cloud computing and big data, the security issues of industrial cyber-physical systems (ICPSs) have become more complicated. Meanwhile, a lot of current security research lacks adaptation to industrial system upgrades. In this paper, an edge-cloud synergy framework for security decision-making is proposed, which takes advantage of the huge convenience and advantages brought by cloud computing and edge computing, and can make security decisions on a global perspective. Under this framework, a combination of Bayesian network-based risk assessment and stochastic game model-based security decision-making is proposed to generate an optimal defense strategy to minimize system losses. This method trains models in the clouds and infers at the edge computing nodes to achieve rapid defense strategy generation. Finally, a case study on the hardware-in-the-loop simulation platform proves the feasibility of the approach.
2021-04-08
Shi, S., Li, J., Wu, H., Ren, Y., Zhi, J..  2020.  EFM: An Edge-Computing-Oriented Forwarding Mechanism for Information-Centric Networks. 2020 3rd International Conference on Hot Information-Centric Networking (HotICN). :154–159.
Information-Centric Networking (ICN) has attracted much attention as a promising future network design, which presents a paradigm shift from host-centric to content-centric. However, in edge computing scenarios, there is still no specific ICN forwarding mechanism to improve transmission performance. In this paper, we propose an edge-oriented forwarding mechanism (EFM) for edge computing scenarios. The rationale is to enable edge nodes smarter, such as acting as agents for both consumers and providers to improve content retrieval and distribution. On the one hand, EFM can assist consumers: the edge router can be used either as a fast content repository to satisfy consumers’ requests or as a smart delegate of consumers to request content from upstream nodes. On the other hand, EFM can assist providers: EFM leverages the optimized in-network recovery/retransmission to detect packet loss or even accelerate the content distribution. The goal of our research is to improve the performance of edge networks. Simulation results based on ndnSIM indicate that EFM can enable efficient content retrieval and distribution, friendly to both consumers and providers.
2021-02-08
Nikouei, S. Y., Chen, Y., Faughnan, T. R..  2018.  Smart Surveillance as an Edge Service for Real-Time Human Detection and Tracking. 2018 IEEE/ACM Symposium on Edge Computing (SEC). :336—337.

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

Chiang, M., Lau, S..  2011.  Automatic multiple faces tracking and detection using improved edge detector algorithm. 2011 7th International Conference on Information Technology in Asia. :1—5.

The automatic face tracking and detection has been one of the fastest developing areas due to its wide range of application, security and surveillance application in particular. It has been one of the most interest subjects, which suppose but yet to be wholly explored in various research areas due to various distinctive factors: varying ethnic groups, sizes, orientations, poses, occlusions and lighting conditions. The focus of this paper is to propose an improve algorithm to speed up the face tracking and detection process with the simple and efficient proposed novel edge detector to reject the non-face-likes regions, hence reduce the false detection rate in an automatic face tracking and detection in still images with multiple faces for facial expression system. The correct rates of 95.9% on the Haar face detection and proposed novel edge detector, which is higher 6.1% than the primitive integration of Haar and canny edge detector.

Li, W., Li, L..  2009.  A Novel Approach for Vehicle-logo Location Based on Edge Detection and Morphological Filter. 2009 Second International Symposium on Electronic Commerce and Security. 1:343—345.

Vehicle-logo location is a crucial step in vehicle-logo recognition system. In this paper, a novel approach of the vehicle-logo location based on edge detection and morphological filter is proposed. Firstly, the approximate location of the vehicle-logo region is determined by the prior knowledge about the position of the vehicle-logo; Secondly, the texture measure is defined to recognize the texture of the vehicle-logo background; Then, vertical edge detection is executed for the vehicle-logo background with the horizontal texture and horizontal edge detection is implemented for the vehicle-logo background with the vertical texture; Finally, position of the vehicle-logo is located accurately by mathematical morphology filter. Experimental results show the proposed method is effective.

Wang Xiao, Mi Hong, Wang Wei.  2010.  Inner edge detection of PET bottle opening based on the Balloon Snake. 2010 2nd International Conference on Advanced Computer Control. 4:56—59.

Edge detection of bottle opening is a primary section to the machine vision based bottle opening detection system. This paper, taking advantage of the Balloon Snake, on the PET (Polyethylene Terephthalate) images sampled at rotating bottle-blowing machine producing pipelines, extracts the opening. It first uses the grayscale weighting average method to calculate the centroid as the initial position of Snake and then based on the energy minimal theory, it extracts the opening. Experiments show that compared with the conventional edge detection and center location methods, Balloon Snake is robust and can easily step over the weak noise points. Edge extracted thorough Balloon Snake is more integral and continuous which provides a guarantee to correctly judge the opening.

Qiao, B., Jin, L., Yang, Y..  2016.  An Adaptive Algorithm for Grey Image Edge Detection Based on Grey Correlation Analysis. 2016 12th International Conference on Computational Intelligence and Security (CIS). :470—474.

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

Geetha, C. R., Basavaraju, S., Puttamadappa, C..  2013.  Variable load image steganography using multiple edge detection and minimum error replacement method. 2013 IEEE Conference on Information Communication Technologies. :53—58.

This paper proposes a steganography method using the digital images. Here, we are embedding the data which is to be secured into the digital image. Human Visual System proved that the changes in the image edges are insensitive to human eyes. Therefore we are using edge detection method in steganography to increase data hiding capacity by embedding more data in these edge pixels. So, if we can increase number of edge pixels, we can increase the amount of data that can be hidden in the image. To increase the number of edge pixels, multiple edge detection is employed. Edge detection is carried out using more sophisticated operator like canny operator. To compensate for the resulting decrease in the PSNR because of increase in the amount of data hidden, Minimum Error Replacement [MER] method is used. Therefore, the main goal of image steganography i.e. security with highest embedding capacity and good visual qualities are achieved. To extract the data we need the original image and the embedding ratio. Extraction is done by taking multiple edges detecting the original image and the data is extracted corresponding to the embedding ratio.

Xu, P., Miao, Q., Liu, T., Chen, X..  2015.  Multi-direction Edge Detection Operator. 2015 11th International Conference on Computational Intelligence and Security (CIS). :187—190.

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

Moussa, Y., Alexan, W..  2020.  Message Security Through AES and LSB Embedding in Edge Detected Pixels of 3D Images. 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES). :224—229.

This paper proposes an advanced scheme of message security in 3D cover images using multiple layers of security. Cryptography using AES-256 is implemented in the first layer. In the second layer, edge detection is applied. Finally, LSB steganography is executed in the third layer. The efficiency of the proposed scheme is measured using a number of performance metrics. For instance, mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean absolute error (MAE) and entropy.

Prathusha, P., Jyothi, S., Mamatha, D. M..  2018.  Enhanced Image Edge Detection Methods for Crab Species Identification. 2018 International Conference on Soft-computing and Network Security (ICSNS). :1—7.

Automatic Image Analysis, Image Classification, Automatic Object Recognition are some of the aspiring research areas in various fields of Engineering. Many Industrial and biological applications demand Image Analysis and Image Classification. Sample images available for classification may be complex, image data may be inadequate or component regions in the image may have poor visibility. With the available information each Digital Image Processing application has to analyze, classify and recognize the objects appropriately. Pre-processing, Image segmentation, feature extraction and classification are the most common steps to follow for Classification of Images. In this study we applied various existing edge detection methods like Robert, Sobel, Prewitt, Canny, Otsu and Laplacian of Guassian to crab images. From the conducted analysis of all edge detection operators, it is observed that Sobel, Prewitt, Robert operators are ideal for enhancement. The paper proposes Enhanced Sobel operator, Enhanced Prewitt operator and Enhanced Robert operator using morphological operations and masking. The novelty of the proposed approach is that it gives thick edges to the crab images and removes spurious edges with help of m-connectivity. Parameters which measure the accuracy of the results are employed to compare the existing edge detection operators with proposed edge detection operators. This approach shows better results than existing edge detection operators.

Wang, R., Li, L., Hong, W., Yang, N..  2009.  A THz Image Edge Detection Method Based on Wavelet and Neural Network. 2009 Ninth International Conference on Hybrid Intelligent Systems. 3:420—424.

A THz image edge detection approach based on wavelet and neural network is proposed in this paper. First, the source image is decomposed by wavelet, the edges in the low-frequency sub-image are detected using neural network method and the edges in the high-frequency sub-images are detected using wavelet transform method on the coarsest level of the wavelet decomposition, the two edge images are fused according to some fusion rules to obtain the edge image of this level, it then is projected to the next level. Afterwards the final edge image of L-1 level is got according to some fusion rule. This process is repeated until reaching the 0 level thus to get the final integrated and clear edge image. The experimental results show that our approach based on fusion technique is superior to Canny operator method and wavelet transform method alone.

2021-01-25
Mao, J., Li, X., Lin, Q., Guan, Z..  2020.  Deeply understanding graph-based Sybil detection techniques via empirical analysis on graph processing. China Communications. 17:82–96.
Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.
2021-01-15
Younus, M. A., Hasan, T. M..  2020.  Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform. 2020 International Conference on Computer Science and Software Engineering (CSASE). :186—190.
DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today's life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.
2021-01-11
Bhat, P., Batakurki, M., Chari, M..  2020.  Classifier with Deep Deviation Detection in PoE-IoT Devices. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). :1–3.
With the rapid growth in diversity of PoE-IoT devices and concept of "Edge intelligence", PoE-IoT security and behavior analysis is the major concern. These PoE-IoT devices lack visibility when the entire network infrastructure is taken into account. The IoT devices are prone to have design faults in their security capabilities. The entire network may be put to risk by attacks on vulnerable IoT devices or malware might get introduced into IoT devices even by routine operations such as firmware upgrade. There have been various approaches based on machine learning(ML) to classify PoE-IoT devices based on network traffic characteristics such as Deep Packet Inspection(DPI). In this paper, we propose a novel method for PoE-IoT classification where ML algorithm, Decision Tree is used. In addition to classification, this method provides useful insights to the network deployment, based on the deviations detected. These insights can further be used for shaping policies, troubleshooting and behavior analysis of PoE-IoT devices.
2020-12-11
Zhou, Z., Yang, Y., Cai, Z., Yang, Y., Lin, L..  2019.  Combined Layer GAN for Image Style Transfer*. 2019 IEEE International Conference on Computational Electromagnetics (ICCEM). :1—3.

Image style transfer is an increasingly interesting topic in computer vision where the goal is to map images from one style to another. In this paper, we propose a new framework called Combined Layer GAN as a solution of dealing with image style transfer problem. Specifically, the edge-constraint and color-constraint are proposed and explored in the GAN based image translation method to improve the performance. The motivation of the work is that color and edge are fundamental vision factors for an image, while in the traditional deep network based approach, there is a lack of fine control of these factors in the process of translation and the performance is degraded consequently. Our experiments and evaluations show that our novel method with the edge and color constrains is more stable, and significantly improves the performance compared with the traditional methods.

2020-08-03
LiPing, Yuan, Pin, Han.  2019.  Research of Low-Quality Laser Security Code Enhancement Technique. 2019 Chinese Automation Congress (CAC). :793–796.
The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
2020-06-26
Karthika, P., Babu, R. Ganesh, Nedumaran, A..  2019.  Machine Learning Security Allocation in IoT. 2019 International Conference on Intelligent Computing and Control Systems (ICCS). :474—478.

The progressed computational abilities of numerous asset compelled gadgets mobile phones have empowered different research zones including picture recovery from enormous information stores for various IoT applications. The real difficulties for picture recovery utilizing cell phones in an IoT situation are the computational intricacy and capacity. To manage enormous information in IoT condition for picture recovery a light-weighted profound learning base framework for vitality obliged gadgets. The framework initially recognizes and crop face areas from a picture utilizing Viola-Jones calculation with extra face classifier to take out the identification issue. Besides, the utilizes convolutional framework layers of a financially savvy pre-prepared CNN demonstrate with characterized highlights to speak to faces. Next, highlights of the huge information vault are listed to accomplish a quicker coordinating procedure for constant recovery. At long last, Euclidean separation is utilized to discover comparability among question and archive pictures. For exploratory assessment, we made a nearby facial pictures dataset it including equally single and gathering face pictures. In the dataset can be utilized by different specialists as a scale for examination with other ongoing facial picture recovery frameworks. The trial results demonstrate that our planned framework beats other cutting edge highlight extraction strategies as far as proficiency and recovery for IoT-helped vitality obliged stages.

Maria Verzegnassi, Enrico Giulio, Tountas, Konstantinos, Pados, Dimitris A., Cuomo, Francesca.  2019.  Data Conformity Evaluation: A Novel Approach for IoT Security. 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). :842—846.

We consider the problem of attack detection for IoT networks based only on passively collected network parameters. For the first time in the literature, we develop a blind attack detection method based on data conformity evaluation. Network parameters collected passively, are converted to their conformity values through iterative projections on refined L1-norm tensor subspaces. We demonstrate our algorithmic development in a case study for a simulated star topology network. Type of attack, affected devices, as well as, attack time frame can be easily identified.

Jiang, Jianguo, Chen, Jiuming, Gu, Tianbo, Choo, Kim-Kwang Raymond, Liu, Chao, Yu, Min, Huang, Weiqing, Mohapatra, Prasant.  2019.  Anomaly Detection with Graph Convolutional Networks for Insider Threat and Fraud Detection. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :109—114.

Anomaly detection generally involves the extraction of features from entities' or users' properties, and the design of anomaly detection models using machine learning or deep learning algorithms. However, only considering entities' property information could lead to high false positives. We posit the importance of also considering connections or relationships between entities in the detecting of anomalous behaviors and associated threat groups. Therefore, in this paper, we design a GCN (graph convolutional networks) based anomaly detection model to detect anomalous behaviors of users and malicious threat groups. The GCN model could characterize entities' properties and structural information between them into graphs. This allows the GCN based anomaly detection model to detect both anomalous behaviors of individuals and associated anomalous groups. We then evaluate the proposed model using a real-world insider threat data set. The results show that the proposed model outperforms several state-of-art baseline methods (i.e., random forest, logistic regression, SVM, and CNN). Moreover, the proposed model can also be applied to other anomaly detection applications.

Niedermaier, Matthias, Fischer, Florian, Merli, Dominik, Sigl, Georg.  2019.  Network Scanning and Mapping for IIoT Edge Node Device Security. 2019 International Conference on Applied Electronics (AE). :1—6.

The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.

Shengquan, Wang, Xianglong, Li, Ang, Li, Shenlong, Jiang.  2019.  Research on Iris Edge Detection Technology based on Daugman Algorithm. 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :308—311.

In the current society, people pay more and more attention to identity security, especially in the case of some highly confidential or personal privacy, one-to-one identification is particularly important. The iris recognition just has the characteristics of high efficiency, not easy to be counterfeited, etc., which has been promoted as an identity technology. This paper has carried out research on daugman algorithm and iris edge detection.

2020-05-08
Hafeez, Azeem, Topolovec, Kenneth, Awad, Selim.  2019.  ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks. 2019 15th International Computer Engineering Conference (ICENCO). :29—38.
Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is used for communication between in-vehicle control networks (IVN). The absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring confidentiality and integrity of transmitted messages via CAN, a new technique has emerged among others to approve its reliability in fully authenticating the CAN messages. At the physical layer of the communication system, the method of fingerprinting the messages is implemented to link the received signal to the transmitting electronic control unit (ECU). This paper introduces a new method to implement the security of modern electric vehicles. The lumped element model is used to characterize the channel-specific step response. ECU and channel imperfections lead to a unique transfer function for each transmitter. Due to the unique transfer function, the step response for each transmitter is unique. In this paper, we use control system parameters as a feature-set, afterward, a neural network is used transmitting node identification for message authentication. A dataset collected from a CAN network with eight-channel lengths and eight ECUs to evaluate the performance of the suggested method. Detection results show that the proposed method achieves an accuracy of 97.4% of transmitter detection.