scholarly journals Detection Mechanisms of One-Pixel Attack

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Wang ◽  
Zhipeng Cai ◽  
Donghyun Kim ◽  
Wei Li
Keyword(s):  
Success Rate ◽  
Deep Neural Network ◽  
Detection Method ◽  
Real Data ◽  
Detection Methods ◽  
Security Threats ◽  
Trigger Detection ◽  
Adversarial Attack ◽  
The One ◽  
Detection Mechanisms

In recent years, a series of researches have revealed that the Deep Neural Network (DNN) is vulnerable to adversarial attack, and a number of attack methods have been proposed. Among those methods, an extremely sly type of attack named the one-pixel attack can mislead DNNs to misclassify an image via only modifying one pixel of the image, leading to severe security threats to DNN-based information systems. Currently, no method can really detect the one-pixel attack, for which the blank will be filled by this paper. This paper proposes two detection methods, including trigger detection and candidate detection. The trigger detection method analyzes the vulnerability of DNN models and gives the most suspected pixel that is modified by the one-pixel attack. The candidate detection method identifies a set of most suspected pixels using a differential evolution-based heuristic algorithm. The real-data experiments show that the trigger detection method has a detection success rate of 9.1%, and the candidate detection method achieves a detection success rate of 30.1%, which can validate the effectiveness of our methods.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals A Fast Orientation Invariant Detector Based on the One-stage Method

2018 ◽  
Vol 232 ◽  
pp. 04036
Author(s):  
Jun Yin ◽  
Huadong Pan ◽  
Hui Su ◽  
Zhonggeng Liu ◽  
Zhirong Peng
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
High Efficiency ◽  
Detection Method ◽  
State Of The Art ◽  
Orientation Angle ◽  
Detection Methods ◽  
Detection Algorithms ◽  
Bounding Boxes ◽  
The One

We propose an object detection method that predicts the orientation bounding boxes (OBB) to estimate objects locations, scales and orientations based on YOLO (You Only Look Once), which is one of the top detection algorithms performing well both in accuracy and speed. Horizontal bounding boxes(HBB), which are not robust to orientation variances, are used in the existing object detection methods to detect targets. The proposed orientation invariant YOLO (OIYOLO) detector can effectively deal with the bird’s eye viewpoint images where the orientation angles of the objects are arbitrary. In order to estimate the rotated angle of objects, we design a new angle loss function. Therefore, the training of OIYOLO forces the network to learn the annotated orientation angle of objects, making OIYOLO orientation invariances. The proposed approach that predicts OBB can be applied in other detection frameworks. In additional, to evaluate the proposed OIYOLO detector, we create an UAV-DAHUA datasets that annotated with objects locations, scales and orientation angles accurately. Extensive experiments conducted on UAV-DAHUA and DOTA datasets demonstrate that OIYOLO achieves state-of-the-art detection performance with high efficiency comparing with the baseline YOLO algorithms.

scholarly journals Estimation of object location probability for object detection using brightness feature only

2019 ◽  
Vol 9 (6) ◽  
pp. 5227
Author(s):  
Hyun Jun Park ◽  
Kwang Baek Kim
Keyword(s):  
Object Detection ◽  
Success Rate ◽  
Detection Method ◽  
Experimental Results ◽  
New Method ◽  
Object Location ◽  
Detection Methods ◽  
Gray Image ◽  
Probability Map

Most existing object detection methods use features such as color, shape, and contour. If there are no consistent features can be used, we need a new object detection method. Therefore, in this paper, we propose a new method for estimating the probability that an object can be located for object detection and generating an object location probability map using only brightness in a gray image. To evaluate the performance of the proposed method, we applied it to gallbladder detection. Experimental results showed 98.02% success rate for gallbladder detection in ultrasonogram. Therefore, the proposed method accurately estimates the object location probability and effectively detected gallbladder.

scholarly journals Detection of DDoS Attacks in Software Defined Networking Using Entropy

2021 ◽  
Vol 12 (1) ◽  
pp. 370
Author(s):  
Cong Fan ◽  
Nitheesh Murugan Kaliyamurthy ◽  
Shi Chen ◽  
He Jiang ◽  
Yiwen Zhou ◽  
...  
Keyword(s):  
Detection Method ◽  
Denial Of Service ◽  
Software Defined Networking ◽  
Attack Detection ◽  
Detection Methods ◽  
Network Architectures ◽  
Security Threats ◽  
Ddos Attacks ◽  
Internet Users ◽  
Detection Effect

Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.

scholarly journals Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3106
Author(s):  
Yabin Yu ◽  
Ying Liu ◽  
Jiawei Chen ◽  
Dong Jiang ◽  
Zilong Zhuang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Identification Accuracy ◽  
Detection Methods ◽  
Single Shot ◽  
Timber Structures ◽  
Structural Failure ◽  
Angle Error ◽  
Bolted Connection ◽  
The One

Bolted connections are widely used in timber structures. Bolt looseness is one of the most important factors leading to structural failure. At present, most of the detection methods for bolt looseness do not achieve a good balance between cost and accuracy. In this paper, the detection method of small angle of bolt loosening in a timber structure is studied using deep learning and machine vision technology. Firstly, three schemes are designed, and the recognition targets are the nut’s own specification number, rectangular mark, and circular mark, respectively. The Single Shot MultiBox Detector (SSD) algorithm is adopted to train the image datasets. The scheme with the smallest identification angle error is the one identifying round objects, of which the identification angle error is 0.38°. Then, the identification accuracy was further improved, and the minimum recognition angle reached 1°. Finally, the looseness in a four-bolted connection and an eight-bolted connection are tested, confirming the feasibility of this method when applied on multi-bolted connection, and realizing a low operating costing and high accuracy.

scholarly journals A Method for Wheat Head Detection Based on Yolov4

2020 ◽  
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection Method ◽  
Computer Hardware ◽  
Detection Methods ◽  
Plant Phenotyping ◽  
Wide Range ◽  
Head Detection ◽  
Multi Level

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

scholarly journals Coarse-Fine-Stitched: A Robust Maritime Horizon Line Detection Method for Unmanned Surface Vehicle Applications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2825 ◽  
Author(s):  
Yuan Sun ◽  
Li Fu
Keyword(s):  
Autonomous Navigation ◽  
Detection Method ◽  
Real Data ◽  
Obstacle Detection ◽  
Line Detection ◽  
Line Segments ◽  
Horizon Line ◽  
The One ◽  
Horizon Line Detection ◽  
Maritime Environment

The horizon line has numerous applications for an unmanned surface vehicles (USV), such as autonomous navigation, attitude estimation, obstacle detection and target tracking. However, maritime horizon line detection is quite a challenging problem. The pixel points of the horizon line features are far fewer than the pixel points of the entire image, on the one hand. Conversely, the detection results might be impacted negatively by the complex maritime environment, waves, light changing, and partial occlusions due to maritime vessels or islands, for example. To solve these problems, a robust horizon line detection method named coarse-fine-stitched (CFS) is proposed in this paper. First, in the coarse step of CFS, a line segment detection approach using gradient features is applied to build a line candidate pool, which probably contains many false detection results. Then, hybrid feature filtering is designed to pick the horizon line segments from the pool in the fine step. Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.

scholarly journals Grasp detection via visual rotation object detection and point cloud spatial feature scoring

2021 ◽  
Vol 18 (6) ◽  
pp. 172988142110555
Author(s):  
Jie Wang ◽  
Shuxiao Li
Keyword(s):  
Object Detection ◽  
Success Rate ◽  
Point Cloud ◽  
Detection Method ◽  
Structural Characteristics ◽  
Point Clouds ◽  
Detection Methods ◽  
Depth Image ◽  
Spatial Feature ◽  
Score Fusion

Accurately detecting the appropriate grasp configurations is the central task for the robot to grasp an object. Existing grasp detection methods usually overlook the depth image or only regard it as a two-dimensional distance image, which makes it difficult to capture the three-dimensional structural characteristics of target object. In this article, we transform the depth image to point cloud and propose a two-stage grasp detection method based on candidate grasp detection from RGB image and spatial feature rescoring from point cloud. Specifically, we first adopt the recently proposed high-performance rotation object detection method for aerial images, named R3Det, to grasp detection task, obtaining the candidate grasp boxes and their appearance scores. Then, point clouds within each candidate grasp box are normalized and evaluated to get the point cloud quality scores, which are fused with the established point cloud quantity scoring model to obtain spatial scores. Finally, appearance scores and their corresponding spatial scores are combined to output high-quality grasp detection results. The proposed method effectively fuses three types of grasp scoring modules, thus is called Score Fusion Grasp Net. Besides, we propose and adopt top-k grasp metric to effectively reflect the success rate of algorithm in actual grasp execution. Score Fusion Grasp Net obtains 98.5% image-wise accuracy and 98.1% object-wise accuracy on Cornell Grasp Dataset, which exceeds the performances of state-of-the-art methods. We also use the robotic arm to conduct physical grasp experiments on 15 kinds of household objects and 11 kinds of adversarial objects. The results show that the proposed method still has a high success rate when facing new objects.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Sign in / Sign up

Export Citation Format

Share Document