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.

scholarly journals A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

2021 ◽  
Vol 22 (2) ◽  
pp. 1177
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Image Features ◽  
Basic Process ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Algorithms ◽  
The Past ◽  
Copy Move Forgery Detection ◽  
Digital Image Forgery

<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

scholarly journals A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

2018 ◽  
Vol 7 (3) ◽  
pp. 345-349
Author(s):  
Anil Gupta
Keyword(s):  
Digital Image ◽  
Feature Point ◽  
Detection Methods ◽  
Forgery Detection ◽  
Point Matching ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Feature Point Matching ◽  
Copy Move Forgery Detection

With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copy-move or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, forgers utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. This research paper illustrates recent issues in the techniques of forgery detection and proposes a advanced copy–move forgery detection scheme using adaptive over-segmentation and feature point matching. The proposed scheme integrates both block-based and key point-based forgery detection methods.

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.

Image Forgery Detection Based on Trigonometric Transforms

2019 ◽  
Vol 28 (2) ◽  
pp. 203-216
Author(s):  
Faten Al-Azrak ◽  
Moawad Dessouky ◽  
Fathi Abd El-Samie ◽  
Ahmed Elkorany ◽  
Zeinab Elsharkawy
Keyword(s):  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Trigonometric Transforms
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