A Robust Method for Detecting Copy-Move Image Forgery Using Stationary Wavelet Transform and Scale Invariant Feature Transform

Das, Taposh; Hasan, Rizbanul; Azam, Md. Rasel; Uddin, Jia

doi:10.1109/ic4me2.2018.8465668

Cited by 16 publications

(12 citation statements)

References 8 publications

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“…Key point-based methods: These techniques use high entropy areas to focus the entire picture. Das et al [44] proposed a fast and robust detection method for this type of picture fraud. The image is first turned to grayscale.…”

Section: Intensity-based Methodsmentioning

confidence: 99%

A Thorough Investigation on Image Forgery Detection

Kumar¹,

Srivastava²

2023

Computer Modeling in Engineering &Amp; Sciences

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Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

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Section: Intensity-based Methodsmentioning

confidence: 99%

A Thorough Investigation on Image Forgery Detection

Kumar¹,

Srivastava²

2023

Computer Modeling in Engineering &Amp; Sciences

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“…al. [20] used the stationary wavelet transform (SWT) to decompose the image and Scale Invariant Feature Transform (SIFT) algorithm to extract features. This method work well, but it produces some false positives.…”

Section: Related Workmentioning

confidence: 99%

False Matches Removing in Copy-Move Forgery Detection Algorithms

Mahdi¹,

Alsaad

2020

MJS

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Today the technology age is characterized by spreading of digital images. The most common form of transfer the information in magazines, newspapers, scientific journals and all types of social media. This huge use of images technology has been accompanied by an evolution in editing tools of image processing which make modifying and editing an image is very simple. Nowadays, the circulation of such forgery images, which distort the truth, has become common, intentionally or unintentionally. Nowadays many methods of copy-move forgery detection which is one of the most important and popular methods of image forgery are available. Most of these methods suffer from the problem of producing false matches as false positives in flat regions. This paper presents an algorithm of the Copy-Move forgery detection using the SIFT algorithm with an effective method to remove the false positives by rejecting all key-points in matches list that own a neighbor less than the threshold. The accuracy of the proposed algorithm was 95 %. The experimental results refer that the proposed method of false positives removing can remove false matches accurately and quickly.

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“…To overcome this problem, many approaches have been studied and steps proposed to speed up or improve various aspects of SIFT. Some recent SIFT-based methods for CMFD are proposed in [51], [52], [53], [54], [55], [56], [57], [58].…”

Section: Keypoint-based Techniquesmentioning

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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A Robust Method for Detecting Copy-Move Image Forgery Using Stationary Wavelet Transform and Scale Invariant Feature Transform

Cited by 16 publications

References 8 publications

A Thorough Investigation on Image Forgery Detection

A Thorough Investigation on Image Forgery Detection

False Matches Removing in Copy-Move Forgery Detection Algorithms

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

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