Image Copy-Move Forgery Detection Based on SURF

Xu, Bo; Wang, Junwen; Liu, Guangjie; Dai, Yuewei

doi:10.1109/mines.2010.189

Cited by 178 publications

(90 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, their method has a high time complexity because the SIFT and grey level features must be extracted together. Bo et al [29] proposed a copy-move forgery detection method based on SURF descriptors. This method is robust against rotation and scaling, but its computational time is high.…”

Section: Keypoint-based Methodsmentioning

confidence: 99%

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Sadeghi

Jalab

Wong

et al. 2017

MJCS

View full text Add to dashboard Cite

With the development of powerful image processing tools and the increasing trend of using images as the main carrier of information, digital image forgery has become an increasingly serious issue. In copy-move forgery, one part of an image is copied and placed elsewhere in the same image. This paper puts forward an effective method based on SIFT for detecting copy-move forgery in digital image. The proposed method can accurately authenticate digital image and locate areas which have been tampered with. The algorithm starts by using scale-invariant features transform (SIFT) to extract local image features, which are known as keypoints, and then searches for similar keypoints based on their Euclidean distances. Finally, the matched keypoints, which represent the copied and pasted areas, are associated with one and another to indicate which parts of the image have been tampered with. Experiments are performed to validate the effectiveness of this method on different attacks, and to quantify its robustness against post-processing. Results show that the method is robust against several geometric processings, including JPEG compression, rotation, noise, and scaling. As a representative result, when considering the standard test dataset MICC-F220, the proposed method achieves true and false positive rates of 100% and 3.12%, respectively.

show abstract

Section: Keypoint-based Methodsmentioning

confidence: 99%

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Sadeghi

Jalab

Wong

et al. 2017

MJCS

View full text Add to dashboard Cite

show abstract

“…These features are computed only on the image itself, without any division, and the extracted features vectors per keypoint are compared with each other to find similar keypoints. Two well-known keypoint-based methods are: Scale Invariant Transform Methods (SIFT) [31,32] and Speeded Up Robust Features (SURF) [33,34]. One of the state of art of keypoint based methods is (Amerini et al, 2011) [32] that proposed a novel method based on SIFT, which is able to examine region duplication forgery and image splicing.…”

Section: )mentioning

confidence: 99%

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Diaa¹,

Mohammed²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Due to the availability of powerful image editing softwares, forgers can tamper the image content easily. There are various types of image forgery, such as image splicing and region duplication forgery. Region duplication is one of the most common manipulations used for tampering digital images. It is vital in image forensics to authenticate the digital image. In this paper, a novel region duplication forgery detection approach is proposed. By segmenting the input image based on the colour features, sufficient number of centroids are produced, that exist even in small or smooth regions. Then, the Least Significant Bit (LSB) of all the colours of pixels in each segment are extracted to build the signature vector. Finally, the hamming distance is calculated through exploiting the signature vector of image to find the dissimilarity. Various experimental results are provided to demonstrate the superior performance of the proposed scheme under some post processing operations such as scaling attack.

show abstract

“…Bo, Guangjie, Junwen, and Yuewei [46], led a research on copy-move image forgery detection by utilizing Speeded-up Robust Features (SURF) formula, and this formula is designed by Herbert Bay et al It includes key point description and detection. They utilized Hessian matrix for finding the respective key points as well as Haar wavelets for setting the orientation.…”

Section: Keypoint-based Techniquesmentioning

confidence: 99%

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Kaur¹

2016

IJCA

View full text Add to dashboard Cite

With the development of Image processing editing tools and software, an image is manipulated very easily. The image manipulation detection is essential for the reason that an image can be employed as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copymove or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, counterfeiters utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. In the literature part, various researchers portrayed the working scenario of copy-move image forgery utilizing the similarity measures as well as the relationship among the original parts of the image and their pasted parts in the similar image. This research paper illustrates recent issues in the techniques of forgery detection and also all their comparative analysis.

show abstract

Image Copy-Move Forgery Detection Based on SURF

Cited by 178 publications

References 4 publications

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Contact Info

Product

Resources

About