A copy-move forgery detection method based on CMFD-SIFT

Yang, Bin; Sun, Xingming; Guo, Honglei; Xia, Zhihua; Chen, Xianyi

doi:10.1007/s11042-016-4289-y

Cited by 86 publications

(23 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…considering holistic matching between set of features on a higher level of consistency to reduce the rate of false alarms and to improve the number of true positives. Copy-move forgery detection can be further classified in terms of block based approaches, which includes various research works in Polar harmonic transform [1], PCA feature [2,3], Zernike moments [4,5], DCT [6,7], and key-point based methods that comprises of SIFT [8,9], ORB [10,11], SURF [12,13], and irregular region-based methods [14,15].…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection in Digital Images using Neural Network

Patel*,

Bhatt

2020

IJITEE

View full text Add to dashboard Cite

Due to easy availability of image editing software applications, many of the digital images are tempered, either to hide some important facts of the image or just to enhance the image. Hence, the integrity of the image is compromised. Thus, in order to preserve the authenticity of an image, it is necessary to develop some algorithms to detect counterfeit parts of an image, if there is any. Two kinds of classic methods exist for the detection of forgery: the key- point based method in which major key points of the image is found and forged part is detected and the block based method that locates the forged part by sectioning the whole image into blocks. Unlike these two classic methods that require multiple stages, our proposed CNN solution provides better image forgery detection. Our experimental results revealed a better forgery detection performance than any other classic approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection in Digital Images using Neural Network

Patel*,

Bhatt

2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…However, this is a relatively computationally expensive approach compared to the other two. The second approach category, keypoint-based methods, includes triangles [31], ORB [32], SURF [32][33][34], and SIFT [35][36][37]. Keypoint approaches are known to be generally fast, but they can fail if source S and destination D show as homogeneous.…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

2019

View full text Add to dashboard Cite

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

show abstract

“…Their experimental results are robust against geometrical transformations. Yang et al [10] presented a novel approach based on key points based analysis. They used SIFT based detector to find the key points for copy move forgery detection.…”

Section: Literature Workmentioning

confidence: 99%

A Technique to Detect Copy-Move Forgery using Enhanced SURF

Srivastava¹,

Kumar²,

Deep³

et al. 2019

IJEAT

View full text Add to dashboard Cite

The field of digital imaging has advanced in recent years with increase of various digital gadgets and applications associated to it. With easily availability of image editing softwares which are either free of cost or budget friendly, image can be effortlessly tampered by the means of making forgery. This has led to increase in crime related to various image processing and computer vision applications. To combat with such forgeries digital forensic provide scientific techniques to identify whether image is original or forged. The proposed work implemented a image forgery check system based on SURF features. This is a pixel based technique where after preprocessing the images, relevant features are extracted and compared with a defined estimated threshold value. Based on the demonstrated results it is decided whether the image has been forged or not and if it is, then the area where tampering has been done is displayed as a forged part. The proposed algorithm is tested using open source CASIA image dataset. Also, the presented result shows that SURF feature based authentication provide forgery detection accuracy of 97%. The results are compared with other techniques in similar domain to prove the novelty of the work.

show abstract

A copy-move forgery detection method based on CMFD-SIFT

Cited by 86 publications

References 36 publications

Copy-Move Forgery Detection in Digital Images using Neural Network

Copy-Move Forgery Detection in Digital Images using Neural Network

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

A Technique to Detect Copy-Move Forgery using Enhanced SURF

Contact Info

Product

Resources

About