RETRACTED ARTICLE: Copy-move forgery detection using SURF feature
         extraction and SVM supervised learning technique

Dhivya, S.; Sangeetha, J.; Sudhakar, B.

doi:10.1007/s00500-020-04795-x

Cited by 44 publications

(19 citation statements)

References 17 publications

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“…The SURF uses the Hessian matrix determinant to approximate the image. The image pixel is expressed as

I ((), x, y) .

When the scale is σ , the Hessian matrix is 16 :

H ((),, x, σ) goodbreak= ([], \begin{array}{l} L_{xx} ((),, x, σ) & L_{xy} ((),, x, σ) \\ L_{yx} ((),, x, σ) & L_{yy} ((),, x, σ) \end{array})

where

L_{xx} ((),, x, σ)

is the second‐order partial derivative of Gaussian

\frac{\partial^{2} g ((), σ)}{\partial x^{2}}

and the convolution of point x result.

L_{xy} ((),, x, σ),

L_{yx} ((),, x, σ),

and

L_{yy} ((),, x, σ)

are the same.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A machine learning‐based method for automatic diagnosis of ankle fracture using X‐ray images

Sun

et al. 2021

Int J Imaging Syst Tech

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Due to the complex mechanism of ankle injury, the clinical diagnosis of ankle fracture is extremely difficult. In order to simplify the fracture diagnosis process, this study proposes an automatic diagnosis model of ankle fractures. Firstly, an ankle fracture classification method suitable for machine learning was developed. By dividing six fracture regions, multiple types of fractures were clarified, and a corresponding dataset was created accordingly. Secondly, the random forest model was used to preprocess the X‐ray images and segment the fracture foreground part of the X‐ray images. Finally, the bag of visual words (BoVW) was used for feature extraction, and classifiers were constructed in different regions for classification. The area under the curve (AUC) of XGBoost was 0.92 ± 0.06. The performance of the XGBoost has proved to be better compared with the SVM when training on a small dataset in each region.

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“…The SURF uses the Hessian matrix determinant to approximate the image. The image pixel is expressed as

I ((), x, y) .

When the scale is σ , the Hessian matrix is 16 :

H ((),, x, σ) goodbreak= ([], \begin{array}{l} L_{xx} ((),, x, σ) & L_{xy} ((),, x, σ) \\ L_{yx} ((),, x, σ) & L_{yy} ((),, x, σ) \end{array})

where

L_{xx} ((),, x, σ)

is the second‐order partial derivative of Gaussian

\frac{\partial^{2} g ((), σ)}{\partial x^{2}}

and the convolution of point x result.

L_{xy} ((),, x, σ),

L_{yx} ((),, x, σ),

and

L_{yy} ((),, x, σ)

are the same.…”

Section: Methodsmentioning

confidence: 99%

“…The flowchart of the SURF-BoVW algorithm is shown in Figure 5. ð Þ: When the scale is σ, the Hessian matrix is 16 :…”

Section: Feature Extractionmentioning

confidence: 99%

A machine learning‐based method for automatic diagnosis of ankle fracture using X‐ray images

Sun

et al. 2021

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…In image forgery detection, copy-move forgery is one of the easiest forms of detection in which detection is performed on some part of the image that is copied and moved to another place within the same image. This kind of detection is used to compare the features of the original image with forgery images [ 27 ]. Extraction of an object: image segmentation and object extraction are closely related terms.…”

Section: Applications Of Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

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Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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“…In 2020, Dhivya1 S., Sangeetha J. and Sudhakar B. [14] proposed a technique used Speed Up Robust (SURF) features extraction, also particular objects are identified using the support vector machine (SVM). The image features are coordinated to find the similar portions in order to reduce the computational unpredictability and to improve the accuracy of false recognition.…”

Section: Related Workmentioning

confidence: 99%

Forged Copy-Move Recognition Using Convolutional Neural Network

Sewan

Altaei

2021

ANJS

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Due to the extreme robust image editing techniques, digital images are subject to multiple manipulations and decreased costs for digital camera and smart phones. Therefore, image credibility is becoming questionable, specifically when images have strong value, such as news report and insurance claims in a crime court. Therefore, image forensic methods test the integrity of the images by applying various highly technical methods set out in the literature. The present work deals with one important research module is the recognition of forged part that applied on copy move forgery images. Two datasets MICC-F2000 and CoMoFoD are used, these datasets are usually adopted in the field of interest. The module concerned with recognizing which is the source image portion and which is the target one of that already detected. Thus, the two detected tampered parts of the image are recognized the original one from them, the other is then referred as forged or tampered part. The proposed module used the buster net of three neural networks that basically adopted the principle of training by using Convolution Neural Network (CNN) to extract the most important features in the images. The first and second networks are parallel working to detect and identify areas that have been tampered with, and then display them through two masks. While the last network classifier takes a copy of these two catchers to decide which is the source image portion from the two detected ones. The achieved recognition results were about F-score 98.98% even if the forged area is rotated or scaled or both of them. Also, the recognition results of the forged image part was 98% when using images do not contributed in the training phase, which refers to that the proposed module is more confident and reliable.

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RETRACTED ARTICLE: Copy-move forgery detection using SURF feature extraction and SVM supervised learning technique

Cited by 44 publications

References 17 publications

A machine learning‐based method for automatic diagnosis of ankle fracture using X‐ray images

A machine learning‐based method for automatic diagnosis of ankle fracture using X‐ray images

Tools, techniques, datasets and application areas for object detection in an image: a review

Forged Copy-Move Recognition Using Convolutional Neural Network

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