scholarly journals A coarse-to-fine copy-move image forgery detection method based on discrete cosine transform

2019 ◽  
Vol 14 (2) ◽  
pp. 843
Author(s):  
Mas Elyna Azol ◽  
Nur Hidayah Ramli ◽  
Y.S. Lee Lee ◽  
Siti Azura Abuzar
Keyword(s):  
Discrete Cosine Transform ◽  
Block Size ◽  
Computation Time ◽  
Recall Rate ◽  
Forgery Detection ◽  
Image Forgery ◽  
Cosine Transform ◽  
Detection Techniques ◽  
Small Block ◽  
Coarse To Fine

<span>Copy-move forgery is a type of image forgery where one part of an image is copied and pasted in other regions of the same image, and it is one of the most common image forgeries to conceal some information in the original image. Discrete Cosine Transform (DCT) is one of the detection techniques which the detection rate relies intensely on the size of block used. Small block size is known for its ability to detect fine cloned objects, but the drawback is it produces too many false positive and requires high execution time. In this research, a method to overcome the weaknesses of using small block size by applying the coarse-to-fine approach with the two-tier process is proposed. The proposed method is evaluated on fifteen forged images on the CoMoFoD dataset. The results demonstrated that the proposed method is able to achieve high precision and recall rate of over 90% as well as improves the computation time by reducing the overall duration of forgery detection up to 73% compared to the traditional DCT method using small block size.  Therefore, these findings validate that the proposed method offers a trade-off between accuracy and runtime.</span>

scholarly journals A Review on Copy-Move Image Forgery Detection Techniques

2021 ◽  
Vol 1892 (1) ◽  
pp. 012010
Author(s):  
Zaid Nidhal Khudhair ◽  
Farhan Mohamed ◽  
Karrar A. Kadhim
Keyword(s):  
Forgery Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Image Forgery Detection

scholarly journals A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 14-34
Author(s):  
Chee Cheun Huang ◽  
Chien Eao Lee ◽  
Vrizlynn L. L. Thing
Keyword(s):  
Detection System ◽  
Recall Rate ◽  
Video Frame ◽  
Forgery Detection ◽  
Feature Classification ◽  
Localization Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Video Forgery Detection ◽  
Video Forgery

Video forgery has been increasing over the years due to the wide accessibility of sophisticated video editing software. A highly accurate and automated video forgery detection system will therefore be vitally important in ensuring the authenticity of forensic video evidences. This article proposes a novel Triangular Polarity Feature Classification (TPFC) video forgery detection framework for video frame insertion and deletion forgeries. The TPFC framework has high precision and recall rates with a simple and threshold-less algorithm designed for real-world applications. System robustness evaluations based on cross validation and different database recording conditions were also performed and validated. Evaluation on the performance of the TPFC framework demonstrated the efficacy of the proposed framework by achieving a recall rate of up to 98.26% and precision rate of up to 95.76%, as well as high localization accuracy on detected forged videos. The TPFC framework is further demonstrated to be capable of outperforming other modern video forgery detection techniques available today.

Copy-move forgery detection technique based on discrete cosine transform blocks features

2020 ◽  
Author(s):  
Esteban Alejandro Armas Vega ◽  
Edgar González Fernández ◽  
Ana Lucila Sandoval Orozco ◽  
Luis Javier García Villalba
Keyword(s):  
Discrete Cosine Transform ◽  
Detection Technique ◽  
Forgery Detection ◽  
Cosine Transform ◽  
Copy Move Forgery Detection

Passive Aproaches for Detecting Image Tampering: A Review

2015 ◽  
Vol 73 (2) ◽  
Author(s):  
Fatma Salman Hashem ◽  
Ghazali Sulong
Keyword(s):  
Digital Image ◽  
Active Area ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Image Tampering ◽  
Highly Active ◽  
Image Forgery Detection ◽  
Hidden Data ◽  
Digital Image Forgery

This paper defines the presently used methods and approaches in the domain of digital image forgery detection.  A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Passive, or blind, approaches for detecting image tampering are regarded as a new direction of research. In recent years, there has been significant work performed in this highly active area of research. Passive approaches do not depend on hidden data to detect image forgeries, but only utilize the statistics and/or content of the image in question to verify its genuineness. The specific types of forgery detection techniques are discussed below. 

scholarly journals A Survey on Photo Forgery Detection Methods

2018 ◽  
Vol 22 ◽  
pp. 01055
Author(s):  
Bilgehan Gurunlu ◽  
Serkan Ozturk
Keyword(s):  
Image Processing ◽  
Data Hiding ◽  
Detection Methods ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Detection Techniques ◽  
Passive Detection ◽  
Image Forgery Detection ◽  
Digital Image Forgery

In recent years, digital image forgery detection has become one of the hardest studying area for researchers investigations in the field of information security and image processing. Image forgery detection methods can be divided into two extensive groups such as Active methods and Passive (Blind) methods. Active methods have been used data hiding techniques like watermarking and digital signatures. Passive forensic methods (or Blind) use image statistics or they investigate the attributes of the image to determine the forgeries. Passive detection techniques are also split into three branches; image splicing, image retouching, copy-move. Such image forgery detection methods are focus of this paper.

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