Detection of Copy-move Forgery in Digital Images Using Radon Transformation and Phase Correlation

Nguyễn, Hiếu Cường; Katzenbeisser, Stefan

doi:10.1109/iih-msp.2012.38

Cited by 17 publications

(12 citation statements)

References 7 publications

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“…A feature vector is extracted for each block and these vectors are matched by calculating the distance between them. This distance can be Euclidean distance [8,9], Hamming distance [10], Hausdorff distance [11], logical distance [12], correlation coefficient [13,14], phase correlation [15,16], or local sensitive hashing [17,18]. The main concern with block-based methods is their computational complexity.…”

Section: Copy-move Forgerymentioning

confidence: 99%

Blind copy-move forgery detection using SVD and KS test

2020

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In this paper, we present a new copy-move forgery detection method based on singular value decomposition (SVD) and the Kolmogorov Smirnov (KS) test. This work introduces a new method of detecting copy-move forgery in images with accuracy up to the pixel level using only 4 features per image block. The proposed method consists of three steps. First, an image is partitioned into blocks of size 16 × 16. Second, image features are extracted from each block using steerable pyramid and SVD transforms. Finally, the extracted features are sorted lexicographically and matched using the KS test. The performance of the proposed method is evaluated using the CoMoFoD database. Four post-processing techniques are considered, namely brightness change, contrast adjustment, color reduction, and image blurring. This method achieved a high precision of more than 95% for 3 of the 4 post-processing techniques. The fourth post-processing (i.e., image blurring), we achieved a precision of 75% which is considerably high for such forgery. In addition, the proposed method outperformed published methods when the images were subjected to brightness change, contrast adjustment, color reduction and image blurring. Finally, the performance of the proposed algorithm shown to provide better precision using fewer features compared to several well-known techniques in the literature.

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Section: Copy-move Forgerymentioning

confidence: 99%

Blind copy-move forgery detection using SVD and KS test

2020

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“…original image and image retouching. [12] introduced a method known as copy moved forgery detection. This method uses discrete cosine transform (DCT) for the represent a feature for the overlapping block.…”

Section: Fig5 Image Splicingmentioning

confidence: 99%

Image Forgery Detection Using Dct And Quantization Matrix Techniques

Abraham*,

Rodrigues,

Fernandes

2019

IJEAT

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In today’s era the image has become useful for communication purpose. But due to the development of software and various techniques it is possible to change images in adding or removing essential feature from it without leaving a clue of real image. It is not easy for the common people to identify whether the image original or tampered. In order to avoid this problem, forgery detection came into existence. Detection of forgery refers to task of image processing to identify that the images are unique or tampered. Several techniques have been used in order to detect the forgeries from the forged image, but this issue has not yet solved. In order to solve these issues we have used Discrete Cosine Transformation (DCT) and quantization matrix techniques for identifying forged areas of image, where the quality of image is not reduced. The Discrete Cosine Transformation (DCT) is used in order for characterizing the overlapping blocks and quantization matrix is used to compress DCT values and gives both highly compressed and best decompressed image quality. Here we use block matching algorithm. This algorithm one of the most frequently used for detecting image which is duplicate. This proposed work also supports for different kinds of images such as JPEG, JPG or PNG of any size it can be either mxn or nxn.

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“…This prompts more proficient and exact classifier. (9) Where h (i, j) shows the relative frequencies; I and j-pixel couple values of image. h(i, j) can be derived as follow:…”

Section: ) Principal Component Analysismentioning

confidence: 99%

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

Shanthi¹,

Raj²

2018

IJET

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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, s uch 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.

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Detection of Copy-move Forgery in Digital Images Using Radon Transformation and Phase Correlation

Cited by 17 publications

References 7 publications

Blind copy-move forgery detection using SVD and KS test

Blind copy-move forgery detection using SVD and KS test

Image Forgery Detection Using Dct And Quantization Matrix Techniques

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

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