DWT Blind Detection Algorithm of Digital Watermarking on still Image Based on KFDA

2013 ◽  
Vol 373-375 ◽  
pp. 454-458
Author(s):  
Hao Long ◽  
Na Huo ◽  
Yong Yang ◽  
Ben Chen Yu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Digital Watermarking ◽  
Detection Algorithm ◽  
Discrete Wavelet ◽  
Blind Detection ◽  
Still Image ◽  
Test Information ◽  
Analysis Theory ◽  
Learning Machine

The algorithm of blind detection on DWT (Discrete Wavelet Transform) digital watermarking of still image is proposed to overcome the lower detection rate and higher false alarm rate problem. The algorithm utilizes KFDA (Kernel Fisher Discrimination Analysis) theory. With the help of research results of blind detection on DCT digital watermarking, the algorithm passes the test information by stochastic resonance system so as to amplify weak signals. Then the algorithm chooses suitable sample vector by computation. KFDA theory, a kind of learning machine with high precision is used to realize blind detection. Both theoretical analysis and simulation results show that the algorithm improves detection probability at low embedding strength. At the same time the algorithm also decreases false alarm rate.

scholarly journals MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 467-472
Author(s):  
I. Pölönen ◽  
K. Riihiaho ◽  
A.-M. Hakola ◽  
L. Annala
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Data Gathering ◽  
Classification Algorithm ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Computationally Efficient ◽  
Learning Machine ◽  
Minimal Learning Machine

Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.

scholarly journals Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2008 ◽  
Author(s):  
Bruna G. Palm ◽  
Dimas I. Alves ◽  
Mats I. Pettersson ◽  
Viet T. Vu ◽  
Renato Machado ◽  
...  
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Reference Image ◽  
Sar Images ◽  
Image Stack ◽  
Military Vehicles ◽  
Wavelength Resolution

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

A Blind Detection Method of Video Steganography Based on Feature Fusion

2015 ◽  
Vol 727-728 ◽  
pp. 867-871
Author(s):  
Wan Qing Wang ◽  
Deng Yin Zhang ◽  
Guang Shuai Shi
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection Method ◽  
Feature Fusion ◽  
Research Area ◽  
Blind Detection ◽  
Video Steganography ◽  
Lower False Alarm Rate ◽  
Steganalysis Method

Due to lack of generalanalysis method in video digital steganalysis research area, a blind detection method which is based onfeature fusion aimed at the video steganography is proposed in this paper.Compared with special steganalysis method, the method has better detection rate,lower false alarm rate, and more extensive applicability.

scholarly journals Automated real-time detection of tonic-clonic seizures using a wearable EMG device

Neurology ◽  
2018 ◽  
Vol 90 (5) ◽  
pp. e428-e434 ◽  
Author(s):  
Sándor Beniczky ◽  
Isa Conradsen ◽  
Oliver Henning ◽  
Martin Fabricius ◽  
Peter Wolf
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Real Time ◽  
Seizure Detection ◽  
Surface Emg ◽  
Detection Algorithm ◽  
Recording Time ◽  
Detection Latency ◽  
Clonic Seizures ◽  
Video Eeg

ObjectiveTo determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device.MethodsWe prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data.ResultsThe mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d.ConclusionsThe performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds.Classification of evidenceThis study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).

scholarly journals Performance Analysis of an Environmental Adaptive CFAR Detector

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Sung Won Hong ◽  
Dong Seog Han
Keyword(s):  
Performance Analysis ◽  
False Alarm ◽  
False Alarm Rate ◽  
Computer Simulations ◽  
Mathematical Analysis ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Constant False Alarm Rate ◽  
Sonar Systems

The constant false alarm rate (CFAR) is a detection algorithm that is generally used in radar or sonar systems, but its performance depends greatly on the environment. This means that the detection performance cannot be satisfied with only a single CFAR detector. This paper evaluates mathematically a proposed environmental adaptive (EA) CFAR detector. The proposed CFAR detector selects an optimal CFAR detector depending on the environment. Computer simulations validate the mathematical analysis and robustness of the detector in homogeneous and nonhomogeneous backgrounds.

scholarly journals Research on Detection and Recognition of Abnormal Data caused by Network Intrusion using Deep Learning

2021 ◽  
Author(s):  
Yan Jian ◽  
Xiaoyang Dong ◽  
Liang Jian
Keyword(s):  
Deep Learning ◽  
False Alarm ◽  
False Alarm Rate ◽  
Data Sets ◽  
Accuracy Rate ◽  
Data Set ◽  
Network Intrusion ◽  
Average Accuracy ◽  
Learning Machine ◽  
Detection And Recognition

Abstract Based on deep learning, this study combined sparse autoencoder (SAE) with extreme learning machine (ELM) to design an SAE-ELM method to reduce the dimension of data features and realize the classification of different types of data. Experiments were carried out on NSL-KDD and UNSW-NB2015 data sets. The results showed that, compared with the K-means algorithm and the SVM algorithm, the proposed method had higher performance. On the NSL-KDD data set, the average accuracy rate of the SAE-ELM method was 98.93%, the false alarm rate was 0.17%, and the missing report rate was 5.36%. On the UNSW-NB2015 data set, the accuracy rate of the SAE-ELM method was 98.88%, the false alarm rate was 0.12%, and the missing report rate was 4.31%. The results show that the SAE-ELM method is effective in the detection and recognition of abnormal data and can be popularized and applied.

An Algorithm of early Smoke Detection Based on Multiple Features

2012 ◽  
Vol 457-458 ◽  
pp. 1254-1257
Author(s):  
Ming Xin Jiang ◽  
Xing Yang Cai ◽  
Hong Yu Wang
Keyword(s):  
Diffusion Process ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Algorithm ◽  
Time Requirement ◽  
Dynamic Features ◽  
Smoke Detection ◽  
Multiple Features ◽  
Color Distribution ◽  
Good Detection

An early smoke detection algorithm based on Codebook model and multiple features is presented in this paper. First, the foreground is obtained by using the Codebook algorithm. Second, the model of color distribution and the model of shape feathers of smoke are applied to detect the suspected smoke area in the foreground. Finally, the false alarm rate is reduced effectively by using dynamic features in the diffusion process of smoke. Experimental results show that our algorithm has good detection performance and achieves real-time requirement which is very important for real application.

scholarly journals Lane Departure Warning Mechanism of Limited False Alarm Rate Using Extreme Learning Residual Network and ϵ-Greedy LSTM

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 644
Author(s):  
Qiaoming Gao ◽  
Huijun Yin ◽  
Weiwei Zhang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Large Scale ◽  
Behavioral Model ◽  
Sensor Data ◽  
Lane Departure Warning ◽  
Residual Network ◽  
Lane Changing ◽  
Lane Departure ◽  
Learning Machine

Neglecting the driver behavioral model in lane-departure-warning systems has taken over as the primary reason for false warnings in human–machine interfaces. We propose a machine learning-based mechanism to identify drivers’ unintended lane-departure behaviors, and simultaneously predict the possibility of driver proactive correction after slight departure. First, a deep residual network for driving state feature extraction is established by combining time series sensor data and three serial ReLU residual modules. Based on this feature network, online extreme learning machine is organized to identify a driver’s behavior intention, such as unconscious lane-departure and intentional lane-changing. Once the system senses unconscious lane-departure before crossing the outermost warning boundary, the ϵ-greedy LSTM module in shadow mode is roused to verify the chances of driving the vehicle back to the original lane. Only those unconscious lane-departures with no drivers’ proactive correction behavior are transferred into the warning module, guaranteeing that the system has a limited false alarm rate. In addition, naturalistic driving data of twenty-one drivers are collected to validate the system performance. Compared with the basic time-to-line-crossing (TLC) method and the TLC-DSPLS method, the proposed warning mechanism shows a large-scale reduction of 12.9% on false alarm rate while maintaining the competitive accuracy rate of about 98.8%.

scholarly journals AN ADAPTIVE SHIP DETECTION ALGORITHM FOR HRWS SAR IMAGES UNDER COMPLEX BACKGROUND: APPLICATION TO SENTINEL1A DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 497-503
Author(s):  
G. He ◽  
Z. Xia ◽  
H. Chen ◽  
K. Li ◽  
Z. Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
False Alarm Rate ◽  
Texture Features ◽  
Detection Algorithm ◽  
Support Vector ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ship Detection ◽  
Wide Swath

Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.

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