A Survey of Partition-Based Techniques for Copy-Move Forgery Detection

Diane, Wandji Nanda Nathalie; Sun, Xingming; Moise, Fah Kue

doi:10.1155/2014/975456

Cited by 29 publications

(2 citation statements)

References 65 publications

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“…In another experimental study at the pixel level, a comparative analysis of the proposed method with other image splicing detection methods in the literature that have localization stage (Krawetz 2007, Ferrara et al 2012, Wandji et al 2014, Lyu et al 2014, Cozzoloni et al 2015, Salloum et al 2017, Destruel et al 2018. Since the performances of the reference studies were presented using the F-measure and MCC metrics, this experiment was also evaluated using them.…”

Section: Experimental Analysesmentioning

confidence: 99%

Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

Yildirim

Tahaoglu

Ulutaş

et al. 2023

Wireless Pers Commun

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With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the stateof-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.

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Section: Experimental Analysesmentioning

confidence: 99%

Color Image Splicing Localization Based on Block Classification Using Transition Probability Matrix

Yildirim

Tahaoglu

Ulutaş

et al. 2023

Wireless Pers Commun

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“…The most public types of passive approaches are image splicing and copy-move (CM). In CM technique, a fragment of the image is copied and embedded into another area in the same image [13], [14]. The splicing technique used fragments of different images and pastes them into another image [15], [16].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 digital x-rays forgery classification model using deep learning

El-Latif

Khalifa

2023

IJ-AI

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Nowadays, the internet has become a typical medium for sharing digital images through web applications or social media and there was a rise in concerns about digital image privacy. Image editing software’s have prepared it incredibly simple to make changes to an image's content without leaving any visible evidence for images in general and medical images in particular. In this paper, the COVID-19 digital x-rays forgery classification model utilizing deep learning will be introduced. The proposed system will be able to identify and classify image forgery (copy-move and splicing) manipulation. Alexnet, Resnet50, and Googlenet are used in this model for feature extraction and classification, respectively. Images have been tampered with in three classes (COVID-19, viral pneumonia, and normal). For the classification of (Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For the classification of (Copy-move forgery, splicing forgery, and no forgery), the model achieves 0.8066 in testing accuracy. Moreover, the model achieves 0.796 and 0.8382 for 6 classes and 9 classes problems respectively. Performance indicators like Recall, Precision, and F1 Score supported the achieved results and proved that the proposed system is efficient for detecting the manipulation in images.<div align="center"> </div>

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