scholarly journals AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

2021 ◽  
Vol 11 (5) ◽  
pp. 4489
Author(s):  
Kalyani Kadam ◽  
Swati Ahirrao ◽  
Ketan Kotecha
Keyword(s):  
Deep Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
High Performance ◽  
Recent Decade ◽  
Learning Approaches ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Explainable Ai

Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.

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.

Research Perception Towards Copy-Move Image Forgery Detection: Challenges and Future Directions

2021 ◽  
pp. 2150054
Author(s):  
Sai Pratheek Chalamalasetty ◽  
Srinivasa Rao Giduturi
Keyword(s):  
High Performance ◽  
Machine Learning Algorithms ◽  
Forgery Detection ◽  
Future Directions ◽  
Image Forgery ◽  
The Past ◽  
Image Forgery Detection ◽  
General Kind ◽  
Copy Move Forgery Detection ◽  
Future Direction

In digital images, Copy-Move Forgery is a general kind of forgery techniques. The process of replicating one part of the image within the same image is termed as copy-move forgery. An effective and reliable approach needs to be developed for identifying these forgeries for restoring the image trustworthiness. The main intent of this paper is to sort out the diverse copy-move image forgery detection models. This survey makes an effective literature analysis on a set of literal works from the past 10 years. The analysis is focused on categorizing the models based on transformation models, machine learning algorithms, and other advanced techniques. The main contribution and limitations of the works are clearly pointed out. In addition, the types of datasets and the simulation platforms utilized by different copy-move forgery detection (CMFD) models are analyzed. The performance measures evaluated by different contributions have been observed for making a concluding decision. The utilization of optimization algorithms on copy-move image forgery detection has also been studied. Finally, the research gaps and challenges with future direction are discussed, which is helpful for researchers in developing an efficient CMFD that could attain high performance.

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

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
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