Comparative Analysis of Different Keypoint Based Copy-Move Forgery Detection Methods

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v22.i2.pp1177-1190 ◽

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>

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

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v7i3.754 ◽

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.

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Copy-Move Forgery Detection Methods: A Critique

Advances in Information Communication Technology and Computing - Lecture Notes in Networks and Systems ◽

10.1007/978-981-15-5421-6_49 ◽

2020 ◽

pp. 501-523

Author(s):

Monika Kharanghar ◽

Amit Doegar

Keyword(s):

Detection Methods ◽

Forgery Detection ◽

Copy Move Forgery Detection

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Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

Symmetry ◽

10.3390/sym12040492 ◽

2020 ◽

Vol 12 (4) ◽

pp. 492 ◽

Cited By ~ 2

Author(s):

Jun Young Park ◽

Tae An Kang ◽

Yong Ho Moon ◽

Il Kyu Eom

Keyword(s):

Local Binary Pattern ◽

Detection Algorithm ◽

Detection Methods ◽

Detection Accuracy ◽

Forgery Detection ◽

Scale Invariant ◽

Region Duplication ◽

Invariant Feature ◽

Copy Move Forgery Detection ◽

Scale Invariant Feature

Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.

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