scholarly journals Inter-frame forgery detection and localisation in videos using earth mover's distance metric

2020 ◽  
Author(s):  
Priyadharsini Selvaraj ◽  
Muneeswaran Karuppiah
Keyword(s):  
Earth Mover’S Distance ◽  
Distance Metric ◽  
Forgery Detection ◽  
Earth Mover's Distance ◽  
Inter Frame

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

scholarly journals An Overview of Digital Video Tampering Detection Using Passive Methods and D-Hash Algorithm

2018 ◽  
Vol 7 (4.6) ◽  
pp. 373
Author(s):  
Anto Crescentia.A ◽  
Sujatha. G
Keyword(s):  
Digital Video ◽  
Forgery Detection ◽  
Tampering Detection ◽  
Integrity Verification ◽  
Hash Algorithm ◽  
Area Of Concern ◽  
Detection Strategies ◽  
Inter Frame ◽  
Passive Techniques ◽  
Integrity Check

Video tampering and integrity detection can be defined as methods of alteration of the contents of the video which will enable it to hide objects, an occasion or adjust the importance passed on by the collection of images in the video. Modification of video contents is growing rapidly due to the expansion of the video procurement gadgets and great video altering programming devices. Subsequently verification of video files is transforming into something very vital. Video integrity verification aims to search out the hints of altering and subsequently asses the realness and uprightness of the video. These strategies might be ordered into active and passive techniques. Therefore our area of concern in this paper is to present our views on different passive video tampering detection strategies and integrity check. Passive video tampering identification strategies are grouped into consequent three classifications depending on the type of counterfeiting as: Detection of double or multiple compressed videos, Region altering recognition and Video inter-frame forgery detection. So as to detect the tampering of the video, it is split into frames and hash is generated for a group of frames referred to as Group of Pictures. This hash value is verified by the receiver to detect tampering.    

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