A new copy-move forgery detection algorithm using image preprocessing procedure

Kuznetsov, Andrey; Myasnikov, Vladislav

doi:10.1016/j.proeng.2017.09.671

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…Although there are a lot of studies in the literature about copy-paste forgery detection, copy-move algorithms basically consist of the following steps: pre-processing step, feature extraction step, matching step, and visualization step (known also as decision stage) [4]. Pre-processing step is not always prerequisite for detection algorithms but optional and it mostly covers filtering for eliminating noise or color space conversion for reducing the dimension of the image or enhancing some descriptive features [5][6][7]. Feature extraction part aims to find out useful information capturing the characteristics of interest in the image.…”

Section: Copy-move Forgery Detection Techniquesmentioning

confidence: 99%

Analysis of Intentional Noise Insertion Approach on the Copy-Move Forgery Detection in Digital Image

Özbay¹

2021

ijisae

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In this digital age, there has been ever-increasing trend on the amount of digital image data. On the other side, in parallel with this situation, image tampering and manipulation attacks have become widespread by the use of various image editing software packages. It means that digital image data have become more vulnerable to tampering or manipulation through forgeries. Copy-move duplication is a type of image forgery involving the process of copying and then pasting an image part from one region to another location within the same image. This paper proposes a novel enhancement on the copy-move forgery detection algorithms. The proposed strategy is based on adding specific noise on the image in order to reach more robust detection. It is observed from the experimental results that, with the specific noise insertion, the copy-move forgery detection algorithms can obtain better performances in comparison to conventional methods. It is found at image level that, by the proposed strategy, with 25×25 blocks, 94.16% accuracy rate is reached. The proposed approach also provides to have reasonable precision and recall values, 92.51% and 96.11%, respectively when compared to existing algorithms.

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Section: Copy-move Forgery Detection Techniquesmentioning

confidence: 99%

Analysis of Intentional Noise Insertion Approach on the Copy-Move Forgery Detection in Digital Image

Özbay¹

2021

ijisae

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“…Generally, preprocessed information will usually make the following CMFD processes more efficient, resulting in faster detection speed or higher detection accuracy. Some examples of the preprocessing techniques are conversion of RGB to grayscale [6], [7], [8], HSV [9], [10] or YCbCr [11], [12], [13] color space, local binary pattern (LBP) [14], median filter [9], [15], discrete wavelet transform (DWT) [16], [17], and principal component analysis (PCA) [18]. In this paper, not only conversion and dimensionality reduction techniques, but also block division and segmentation are also included in preprocessing (e.g., SLIC [15], [19]).…”

Section: Preprocessingmentioning

confidence: 99%

Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

Teerakanok

Uehara

2019

IEEE Access

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This paper presents the state-of-the-art technical reviews and analysis of recent copy-move forgery detection (CMFD) techniques. A new CMFD process pipeline was introduced. In addition, the techniques used in each stage of the CMFD pipeline are summarized and classified into small categories. Furthermore, the tables of comparison are provided as a quick reference. This technical review paper is expected to help provide useful insights and updated information regarding recent advancements in CMFD to researchers in the field.

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“…It is an effortless method applied for image enrichment that takes effort by stretching the series of intensity values [14] incorporates to get better contrast in an image and extent preferred choice of values. The upper and lower pixel value limits must be specified before stretching can be performed over which the image is to be normalized.…”

Section: B Preprocessingmentioning

confidence: 99%

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

Aslam¹,

Santhi²,

Ramasamy³

et al. 2019

IJACSA

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There are various mathematical optimization problems that can be effectively solved by meta-heuristic algorithms. The improvement of these algorithms is that they carry out iterative search processes which resourcefully act upon exploration and exploitation in spatial domain containing global and local optima. An innovative robust Cuckoo Optimization Algorithm (COA) with adaptive thresholding is proposed to solve the problem of detection and estimation of surface defects on metal coating surfaces. The proposed method is developed through implementing changes to COA and improved the performance. For improving capability of local search as well to keep the global search effect, the enhanced methods such as level set is associated with the proposed method. Also, the method adapts dynamic step size, adaptively changing with the search process for improving the rate of convergence and the ability of local search. The algorithm performance is scrutinized from the experimental analysis and results. Also, the segmentation effectiveness is further enhanced by adapting suitable methods for preprocessing and post processing. The comparison and analysis of the results accomplished with the proposed method and results of earlier methods shows superior performance of the proposed method.

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A new copy-move forgery detection algorithm using image preprocessing procedure

Cited by 13 publications

References 7 publications

Analysis of Intentional Noise Insertion Approach on the Copy-Move Forgery Detection in Digital Image

Analysis of Intentional Noise Insertion Approach on the Copy-Move Forgery Detection in Digital Image

Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

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