Image Forgery Detection and Localization

2021 ◽  
pp. 176-182
Author(s):  
Aditi Shedge ◽  
Shaily Shah ◽  
Shubham Pandey ◽  
Mansi Pandey ◽  
Rupali Satpute
Keyword(s):  
Human Brain ◽  
Sources Of Information ◽  
Forgery Detection ◽  
Image Splicing ◽  
Tampering Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Image Forgery Detection ◽  
Crime Investigation ◽  
Detection And Localization

A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.

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.

scholarly journals Reviewing the Effectivity Factor in Existing Techniques of Image Forensics

2017 ◽  
Vol 7 (6) ◽  
pp. 3558
Author(s):  
Shashidhar TM ◽  
KB Ramesh
Keyword(s):  
Image Forensics ◽  
Research Trend ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Detection Techniques ◽  
Open Research ◽  
Image Forgery Detection ◽  
Attack Scenario ◽  
Research Gap

Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present research trend with respect to tool adoption, database adoption, and technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques.

scholarly journals Digital Image Forgery Detection

2019 ◽  
Vol 8 (2S3) ◽  
pp. 658-661
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Image Manipulation ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Naked Eye ◽  
Image Forgery Detection ◽  
Crime Investigation ◽  
Digital Image Forgery

With the growing challenges in authenticity and integrity of images, image manipulation has crumbled assurance over digital image. The major motivation of the forgery in image is manipulating the image in such a way that it cannot be distinguished to the naked eye. Image manipulation has increased the demand to assess the trustworthiness of digital images when used in crime investigation, as witness of law and for surveillance purposes. In this paper, various types of image forgery and detection techniques have been explained. Initially different kinds of forgery attacks are categorized and summary of passive approach is discussed

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 Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

scholarly journals Image Forgery Detection and Localization using Modified JPEG Ghost

2019 ◽  
Vol 8 (12) ◽  
pp. 5433-5436
Keyword(s):  
Digital Image ◽  
Original Structure ◽  
Forgery Detection ◽  
Image Forgery ◽  
Digital Devices ◽  
Image Forgery Detection ◽  
Storage Format ◽  
Digital Image Forgery ◽  
Detection And Localization ◽  
Multiple Compression

Authenticity of an image taken digitally suffers severe threats as a result of increase in various powerful digital image editing tools. These tools modifies the image contents without leaving footprint of such modifications. We come up with a technique that analyzes digital image forgery detection in JPEG images which goes through multiple compression. Nearly all digital devices uses JPEG as a standard storage format to maintain the storage space. JPEG is a lossy compression standard. By using any image processing tools, when assailant changes any part of a JPEG image and save it, the alter part of the image has different compression artifacts. JPEG ghost algorithm is used to detect disparity in JPEG blocks that rise from improper alignments of JPEG blocks respect to original structure and detect local footprint of JPEG compression. In our work, our proposed technique will modify JPEG ghost detection to detect and localize digital image forgery.

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. 

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