Image Forgery Detection using Deep Learning and Signal Processing Methods

2021 ◽  
Author(s):  
Owen Mayer
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Forgery Detection ◽  
Processing Methods ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Signal Processing Methods

Copy-Move Image Forgery Detection Using Deep Learning Methods: A Review

2019 ◽  
Author(s):  
Arfa Binti Zainal Abidin ◽  
Hairudin Bin Abdul Majid ◽  
Azurah Binti A Samah ◽  
Haslina Binti Hashim
Keyword(s):  
Deep Learning ◽  
Forgery Detection ◽  
Learning Methods ◽  
Image Forgery ◽  
Image Forgery Detection

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

A Novel Deep learning and Machine Learning powered approach for Image Forgery Detection

2021 ◽  
Author(s):  
N. Nanthini ◽  
S. Sasipriya ◽  
Santosh Kumar Sahoo ◽  
Balachandra Pattanaik ◽  
S.A. Sivakumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

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

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%.

A deep-learning-based image forgery detection framework for controlling the spread of misinformation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ambica Ghai ◽  
Pradeep Kumar ◽  
Samrat Gupta
Keyword(s):  
Deep Learning ◽  
Image Transformation ◽  
Forgery Detection ◽  
Transformation Techniques ◽  
Image Forgery ◽  
Content Type ◽  
Image Forgery Detection ◽  
Benchmark Datasets ◽  
Robust Image ◽  
Practical Implications

PurposeWeb users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.Design/methodology/approachThe proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.FindingsThe comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.Research limitations/implicationsThis study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.Practical implicationsThis study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.Social implicationsIn the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.Originality/valueThis study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

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