scholarly journals Calibrating Thresholds based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 3658-3663
Keyword(s):  
False Positive ◽  
Feature Vector ◽  
Roc Curves ◽  
Optimal Threshold ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Harris Corner ◽  
Corner Points

In this paper, prominent keypoint based features are compared in order to analyze their reliability and efficiency against forgery detection. Four features specifically SURF, KAZE, Harris corner points and BRISK features are used individually on a set of images. The method includes four phases: Image pre-processing, keypoint detection, feature vector description and feature vector matching. In feature matching, MaxRatio has been chosen as a varying parameter for calculating values of false positives and false negatives for each feature. MaxRatio defines the ratio for rejecting ambiguous matches of feature descriptors in the images. The optimal threshold value for MaxRatio is calibrated with the help of trade-off between detection accuracy and false positive ratio. The changes in false negative ratio and false positive ratio are picturized in order to find out optimal threshold for detection accuracy. ROC curves are also plotted for each feature at different values of MaxRatio and area under the ROC curves are calculated. The experiments are performed on two benchmark datasets, namely CASIA version 2.0 and MICC-F600. It has been perceived from experimental outcomes that KAZE features gave best values for all the performance metrics namely accuracy, precision, area under the ROC curve and F1-score with little compromise in time complexity, whereas Harris corner points gave the worst results as compared to rest of the features. Further, in order to improve the execution time, the computation of non-linear scale space process in KAZE can be simplified and GPU programming for real-time performance may also be used.

scholarly journals Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 45 ◽  
Author(s):  
Chuan-Yu Chang ◽  
Kathiravan Srinivasan ◽  
Wei-Chun Wang ◽  
Ganapathy Pattukandan Ganapathy ◽  
Durai Raj Vincent ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
High Performance ◽  
Learning Approaches ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Proposed Model ◽  
Human Eyes

In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.

Implementation of Improved Hash and Mapping Modified Low Power Parallel Bloom Filter Design

2013 ◽  
Vol 7 (4) ◽  
pp. 11-21
Author(s):  
K. Saravanan ◽  
A. Senthilkumar
Keyword(s):  
Low Power ◽  
False Positive ◽  
Filter Design ◽  
Parallel Architecture ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Field Programmable ◽  
Mapping Scheme

In this article, the authors present an investigation on bloom filters and introduce a new improved variant, which uses a secure modified hash function and suggested improved mapping scheme with an efficient parallel architecture. This novel architecture provides efficient, relatively fast membership querying and compact information representation with negligible false positive. This is relatively a low power and secure design with very less false positive ratio when compared with the traditional bloom filters. The design has been evaluated and tested using Xilinx 65 nm Virtex-5 field-programmable gate array as the target technology. The performance matrices are false positive ratio, power, speed and compactness.

Limits of the ‘Mini-Mental State’ as a screening test for dementia and delirium among hospital patients

1982 ◽  
Vol 12 (2) ◽  
pp. 397-408 ◽  
Author(s):  
James C. Anthony ◽  
Linda LeResche ◽  
Unaiza Niaz ◽  
Michael R. Von Korff ◽  
Marshal F. Folstein
Keyword(s):  
Mental State ◽  
False Positive ◽  
Mini Mental State Examination ◽  
False Negative ◽  
General Medical Ward ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Hospital Patients ◽  
Screen Instrument ◽  
State Examination

SynopsisWith a psychiatrist's standardized clinical diagnosis as the criterion, the ‘Mini-Mental State’ Examination (MMSE) was 87% sensitive and 82% specific in detecting dementia and delirium among hospital patients on a general medical ward. The false positive ratio was 39% and the false negative ratio was 5 %. All false positives had less than 9 years of education; many were 60 years of age or older. Performance on specific MMSE items was related to education or age. These findings confirm the MMSE's value as a screen instrument for dementia and delirium when later, more intensive diagnostic enquiry is possible; they reinforce earlier suggestions that the MMSE alone cannot yield a diagnosis for these conditions.

scholarly journals Forgery detection algorithm based on texture features

2021 ◽  
Vol 24 (1) ◽  
pp. 226
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil
Keyword(s):  
Local Binary Pattern ◽  
Naive Bayes ◽  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Detection Algorithm ◽  
Naïve Bayes ◽  
The Other ◽  
Detection Accuracy ◽  
Forgery Detection

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques.

An Improved Harris Corner Detection Algorithm for Noised Images

2012 ◽  
Vol 433-440 ◽  
pp. 6151-6156 ◽  
Author(s):  
Xin Zhang ◽  
Xiu Hua Ji
Keyword(s):  
Image Registration ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Corner Detection ◽  
Detection Accuracy ◽  
Image Mosaic ◽  
Harris Corner ◽  
Noise Impact ◽  
Corner Points ◽  
Harris Corner Detection

The Harris corner detection algorithm is widely applied in image mosaic, which is simple and stable. However, the algorithm has a disadvantage that it obtains a lot of false corners when there exist some noise in an image. An improved Harris corner detection algorithm is proposed in this paper. The new algorithm reduces the noise impact greatly. The experimental results show that the improved algorithm not only reduces false corner points greatly, but also retain the majority of true corners. As a result, it improves the detection accuracy and reduces the chance of error matching in image registration.

A Hybrid Approach for Accurate BT Traffic Identification

2010 ◽  
Vol 108-111 ◽  
pp. 279-284
Author(s):  
Ru Hui Zhang ◽  
Ye Du ◽  
Xing Wang ◽  
Zhong Lan Yuan
Keyword(s):  
False Positive ◽  
Hybrid Approach ◽  
Transfer Mechanism ◽  
Preliminary Evaluation ◽  
Traffic Identification ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Information Method ◽  
Flow Information ◽  
Encrypted Traffic

In this paper, a hybrid approach for identifying the traffic running over BitTorrent (BT) protocol is proposed. Besides the conventional port-based and signature-based methods, another two BT-oriented methods dealing with the peer-information and flow-information of BT traffic are also adopted. The peer-information method makes use of the unencrypted peer-transfer mechanism of BT protocol, and the flow-information method focuses on identifying the encrypted traffic, which evades the above three methods, with low false-positive ratio. The preliminary evaluation shows that our hybrid approach is effective and comprehensive for BT traffic identification.

Detecting Application-Layer Attacks Based on Hidden Semi-Markov Models

2014 ◽  
Vol 631-632 ◽  
pp. 923-927
Author(s):  
Bai Lin Xie ◽  
Qian Sheng Zhang
Keyword(s):  
Anomaly Detection ◽  
Markov Models ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Application Layer ◽  
Arrival Times ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Theory Application ◽  
Application Layer Protocol

This paper presents an application-layer attack detection method based on hidden semi-markov models. In this method, the keywords of an application-layer protocol and their inter-arrival times are used as the observations, a hidden semi-markov model is used to describe the application-layer behaviors of a normal user who is using some application-layer protocol. This method is also based anomaly detection. In theory, application-layer anomaly detection can identify the known, unknown and novel attacks happened on application-layer. The experimental results show that this method can identify several application-layer attacks, and has high detection accuracy and low false positive ratio.

scholarly journals A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup

2020 ◽  
Vol 10 (20) ◽  
pp. 7198
Author(s):  
Junghwan Kim ◽  
Myeong-Cheol Ko ◽  
Moon Sun Shin ◽  
Jinsoo Kim
Keyword(s):  
False Positive ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Experimental Result ◽  
Ip Address ◽  
Positive Ratio ◽  
Ip Addresses ◽  
False Positive Ratio ◽  
Ip Address Lookup ◽  
Filter Size

Prefix caching is one of the notable techniques in enhancing the IP address lookup performance which is crucial in packet forwarding. A cached prefix can match a range of IP addresses, so prefix caching leads to a higher cache hit ratio than IP address caching. However, prefix caching has an issue to be resolved. When a prefix is matched in a cache, the prefix cannot be the result without assuring that there is no longer descendant prefix of the matching prefix which is not cached yet. This is due to the aspect of the IP address lookup seeking to find the longest matching prefix. Some prefix expansion techniques avoid the problem, but the expanded prefixes occupy more entries as well as cover a smaller range of IP addresses. This paper proposes a novel prefix caching scheme in which the original prefix can be cached without expansion. In this scheme, for each prefix, a Bloom filter is constructed to be used for testing if there is any matchable descendant. The false positive ratio of a Bloom filter generally grows as the number of elements contained in the filter increases. We devise an elaborate two-level Bloom filter scheme which adjusts the filter size at each level, to reduce the false positive ratio, according to the number of contained elements. The experimental result shows that the proposed scheme achieves a very low cache miss ratio without increasing the number of prefixes. In addition, most of the filter assertions are negative, which means the proposed prefix cache effectively hits the matching prefix using the filter.

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