Review, analysis and parameterisation of techniques for copy–move forgery detection in digital images

Dixit, Rahul; Naskar, Ruchira

doi:10.1049/iet-ipr.2016.0322

Cited by 32 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, a sufficient number of feature vectors should be selected to achieve high accuracy, as suggested by Dixit et al in 2017 [17]. The proposed algorithm has selected 14 sufficient features and appropriate block size of 8 × 8.…”

Section: State Of the Artmentioning

confidence: 99%

Digital image copy-move forgery detection based on discrete fractional wavelet transform

2018

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:With the advancement of sophisticated cameras and image editing software tools, digital image tampering techniques are frequently used without leaving visual cues behind. Digital image copy-move forgery is a kind of image manipulation that involves copying and pasting of a certain section (or sections) within the same digital image. Generally, this is done with false intentions of hiding important information or providing false information in an image. In view of this, the focus of the present paper is to propose a discrete fractional wavelet transform-based scheme for identification of duplicated regions in the image. The test image is split into overlapping image blocks with fixed dimensions. Then, on each image block, discrete fractional wavelet transform is employed for the extraction of their features. All the feature vectors are systematized in lexicographical manner followed by the block matching and block filtering steps to obtain the replicated blocks, if any. The proposed method can detect single and multiple duplicated regions successfully. The results are compared to existing techniques based on precision and recall parameters. Simulation results show that the proposed forgery detection scheme can detect tampering areas even in the presence of distortions due to Gaussian blurring and JPEG compression.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Digital image copy-move forgery detection based on discrete fractional wavelet transform

2018

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Therefore, assurance of authenticity of digital images is one of the significant subjects of research. In the field of digital image forensics [1], copy‐move [2] is one of the well‐known technique to forge an image. In this technique, a portion of the image is copied and pasted over different areas of the same image.…”

Section: Introductionmentioning

confidence: 99%

Composite attacks‐based copy‐move image forgery detection using AKAZE and FAST with automatic contrast thresholding

Dixit

Bag

2020

IET image process

View full text Add to dashboard Cite

Copy‐move image forgery is one of the most popular image tampering technique which can be performed for vicious purposes. In this forgery technique, selected region is copied and pasted at different locations on the same image to produce a manipulated image. Such forgery is denigratory as it can alter the image content by hiding or appending visual information. In this study, the authors propose a novel keypoint‐based technique to detect forged images sustaining composite attacks consisting of various combinations of geometrical and post‐processing operations. In the authors' method, AKAZE and FAST techniques are used to extract keypoints from the image. Non‐maximal value suppression with automatic contrast thresholding is performed during FAST keypoint extraction. SIFT and DAISY descriptors are computed corresponding to extracted keypoints. PCA is applied over SIFT and DAISY descriptors to discard lower components which are sensitive to distortions occurred in images. They apply a correlation‐based nearest neighbour search technique to detect similarity among keypoint descriptors. HDBSCAN algorithm is applied to obtain matched keypoint clusters. Further, RANSAC algorithm is utilised for removal of keypoint outliers. In comparison to state‐of‐the‐art techniques, their approach achieve high F‐measure (%) and low FPR (%) for image‐level as well as pixel‐level copy‐move forgery detection.

show abstract

“…In recent years, digital image processing has become a popular and attractive research area. The technology has been widely used in artificial intelligence, industrial detection, and so on, and has promoted the development of relevant disciplines [18,19,20]. Computer vision provides another new way to measure water content of crude oil.…”

Section: Introductionmentioning

confidence: 99%

A Visual Measurement of Water Content of Crude Oil Based on Image Grayscale Accumulated Value Difference

Liu

Chu

Peng

et al. 2019

Sensors

View full text Add to dashboard Cite

In the process of oil exploitation, the water level of an oil well can be predicted and the position of reservoir can be estimated by measuring the water content of crude oil, with reference for the automatic production of high efficiency in the oil field. In this paper, a visual measuring method for water content of crude oil is proposed. The oil and water in crude oil samples were separated by centrifugation, distillation or electric dehydration, and a water–oil layered mixture was formed according to the unequal densities. Then the volume ratio of water and oil was analyzed by digital image processing, and the water content and oil content was able to be calculated. A new method for measuring water content of crude oil based on IGAVD (image grayscale accumulated value difference) is proposed, which overcomes the uncertainty caused by environmental illumination and improves the measurement accuracy. In order to verify the effectiveness of the algorithm, a miniaturization and low-cost system prototype was developed. The experimental results show that the average power consumption is about 165 mW and the measuring error is less than 1.0%. At the same time, the real-time and remote transmission about measurement results can be realized.

show abstract

Review, analysis and parameterisation of techniques for copy–move forgery detection in digital images

Cited by 32 publications

References 30 publications

Digital image copy-move forgery detection based on discrete fractional wavelet transform

Digital image copy-move forgery detection based on discrete fractional wavelet transform

Composite attacks‐based copy‐move image forgery detection using AKAZE and FAST with automatic contrast thresholding

A Visual Measurement of Water Content of Crude Oil Based on Image Grayscale Accumulated Value Difference

Contact Info

Product

Resources

About