scholarly journals Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

2021 ◽  
Author(s):  
Wina Permana Sari ◽  
Hisyam Fahmi
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Extraction Process ◽  
Test Accuracy ◽  
Learning Approach ◽  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Level Analysis

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.

scholarly journals Image forgery detection using error level analysis and deep learning

2018 ◽  
Vol 17 (2) ◽  
pp. 653 ◽  
Author(s):  
Ida Bagus Kresna Sudiatmika ◽  
Fathur Rahman ◽  
Trisno Trisno ◽  
Suyoto Suyoto
Keyword(s):  
Deep Learning ◽  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Level Analysis

Image forgery detection by semi-automatic wavelet soft-Thresholding with error level analysis

2017 ◽  
Vol 85 ◽  
pp. 348-356 ◽  
Author(s):  
Daniel Cavalcanti Jeronymo ◽  
Yuri Cassio Campbell Borges ◽  
Leandro dos Santos Coelho
Keyword(s):  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Soft Thresholding ◽  
Image Forgery Detection ◽  
Level Analysis

A Method for Image Forgery Detection Based on Error Level Analysis (ELA) Technique

2020 ◽  
Author(s):  
Emanuele Morra ◽  
Roberto Revetria ◽  
Danilo Pecorino ◽  
Gabriele Galli ◽  
Andrea Mungo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Digital Imaging ◽  
Imaging Techniques ◽  
Automated System ◽  
Learning Approaches ◽  
Forgery Detection ◽  
Error Level ◽  
Detection Algorithms ◽  
Level Analysis

In the last years, there has been growing a large increase in digital imaging techniques, and their applications became more and more pivotal in many critical scenarios. Conversely, hand in hand with this technological boost, imaging forgeries have increased more and more along with their level of precision. In this view, the use of digital tools, aiming to verify the integrity of a certain image, is essential. Indeed, insurance is a field that extensively uses images for filling claim requests and a robust forgery detection is essential. This paper proposes an approach which aims to introduce a full-automated system for identifying potential splicing frauds in images of car plates by overcoming traditional problems using artificial neural networks (ANN). For instance, classic fraud-detection algorithms are impossible to fully automatize whereas modern deep learning approaches require vast training datasets that are not available most of the time. The method developed in this paper uses Error Level Analysis (ELA) performed on car license plates as an input for a trained model which is able to classify license plates in either original or forged.

scholarly journals https://jusst.org/developing-the-rural-economy-structure-in-agriculture-towards-the-betterment-of-indias-economy/

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Amit Doegar ◽  
◽  
Maitreyee Dutta ◽  
Gaurav Kumar ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Rural Economy ◽  
Learning Algorithm ◽  
Learning Model ◽  
Image Features ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Testing Dataset ◽  
Deep Learning Model

In the present scenario, one of the threats of trust on images for digital and online applications as well as on social media. Individual’s reputation can be turnish using misinformation or manipulation in the digital images. Image forgery detection is an approach for detection and localization of forged components in the image which is manipulated. For effective image forgery detection, an adequate number of features are required which can be accomplished by a deep learning model, which does not require manual feature engineering or handcraft feature approaches. In this paper we have implemented GoogleNet deep learning model to extract the image features and employ Random Forest machine learning algorithm to detect whether the image is forged or not. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the dataset into training and testing dataset and also compared with the state-of-the-art approaches.

An efficient method for image forgery detection based on trigonometric transforms and deep learning

2020 ◽  
Vol 79 (25-26) ◽  
pp. 18221-18243
Author(s):  
Faten Maher Al_Azrak ◽  
Ahmed Sedik ◽  
Moawad I. Dessowky ◽  
Ghada M. El Banby ◽  
Ashraf A. M. Khalaf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Efficient Method ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Trigonometric Transforms
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