scholarly journals Intelligent Visual Similarity-Based Phishing Websites Detection

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1681
Author(s):  
Jiann-Liang Chen ◽  
Yi-Wei Ma ◽  
Kuan-Lung Huang
Keyword(s):  
Visual Similarity ◽  
Color Histogram ◽  
Scale Invariant Feature Transform ◽  
Local Similarity ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Visual Technique ◽  
Perceptual Hashing ◽  
Feature Transform ◽  
Scale Invariant Feature

This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of ‘very similar’, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of ‘local similarity’, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns ‘very similar’ and ‘local similar’ cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.

Implementation of SIFT for detection of electronic waste

2018 ◽  
Vol 7 (2.8) ◽  
pp. 353
Author(s):  
A Roshna Meeran ◽  
V Nithya
Keyword(s):  
Image Processing ◽  
Electronic Waste ◽  
Scale Invariant Feature Transform ◽  
Recycling Process ◽  
Scale Invariant ◽  
Object Matching ◽  
Invariant Feature ◽  
Detection And Identification ◽  
Feature Transform ◽  
Scale Invariant Feature

The paper focuses on the investigation of image processing of Electronic waste detection and identification in recycling process of all Electronic items. Some of actually collected images of E-wastes would be combined with other wastes. For object matching with scale in-variance the SIFT (Scale -Invariant- Feature Transform) is applied. This method detects the electronic waste found among other wastes and also estimates the amount of electronic waste detected the give set of wastes. The detection of electronics waste by this method is most efficient ways to detect automatically without any manual means.

scholarly journals Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor

2017 ◽  
Vol 14 (3) ◽  
pp. 651-661 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Nearest Neighbor ◽  
Daily Basis ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Suggested Approach ◽  
Approximate Nearest Neighbor ◽  
Invariant Feature ◽  
Key Points ◽  
Feature Transform ◽  
Scale Invariant Feature

There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming. In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process. The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description.

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