Authentication

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Authentication

In the context of bonds, refers to the validation of a bond certificate.

Authentication

A legal certification that a document is genuine. In business, this is especially applied to bonds, showing that an issue was legitimate.
References in periodicals archive ?
The region duplication forgery detection methods have been categorized and evaluated based on their sensitivity towards two types of attacks: a) Geometrical manipulation attacks and b) Post-processing attacks.
In the following, the effect of the post-verification step with or without variance is evaluated based on IMD to prove its essentiality to the forgery detection. Fig.
One is speed which is important when NDD is used for information retrieval, the other one is detection effectiveness which is required when NDD is applied for forgery detection. To meet these requirements, a design of feature vector generator employing PCNN is presented for NDD in this paper.
median filtering, No manipulation gaussian blurring, detection features additive white Gaussian noise, eesampling) detection Bondi [18] Clustering of camera- Tampering Detection No based CNN features and Localization Yarlagadda GAN and one-class Satellite image No [19] classifier forgery detection and Localization D'Avino [20] Autoencoder with Video forgery No recurrent neural detection networks Tuama [21] A layer of Camera model No preprocessing is identification added to the CNN model Bondi [22] data-driven algorithm Camera model No based on convolutional identification neural networks Proposed Multiview feature, Image tamper Yes perceptual saliency, detection semi-supervised hashing
Kao, "Fast copy-move forgery detection," WSEAS Transactions on Signal Processing, vol.
Improvement in Copy -Move Forgery Detection Using Hybrid Approach", I.J.
Copy-move forgery detection and localization by means of robust clustering with J-Linkage.
The proposed method is the first in the literature that adapted a nonlinear scale space based keypoint extraction method into copy move forgery detection. In this section and the latter, we give general outline of the AKAZE keypoint and feature extraction methods and the details of the false match elimination and forgery localization procedures.
Feature Generation for Forgery Detection. In this step, we combined together two features described already with proper coefficient.
The obtained results show that this is a viable method to provide forgery detection to official financial institutions websites.
Forgery detection dates back centuries to the reign of the great Greek and Roman philosophers.
There may also be many counterfeits which evade forgery detection machines in retail and other commercial outlets