Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Zedan, Ibrahim A.; Soliman, Mona; Elsayed, Khaled M.; Onsi, Hoda M.

doi:10.14569/ijacsa.2021.0120729

Cited by 5 publications

(9 citation statements)

References 93 publications

(405 reference statements)

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“…The method used to localize and describe the image keypoints, as well as the detection threshold(s) applied to detect the keypoints, are two key factors that influence the feature extraction phase [5].…”

Section: Feature Extraction Phasementioning

confidence: 99%

“…The matching phase aims to find similar keypoints in an image that signify regions where CMF is initially suspected [5]. Two steps make up the matching phase.…”

Section: Matching Phasementioning

confidence: 99%

“…There are several ways to forge an image, but the cloning forgery is the hardest in its detection and the widespread one [3]. The cloning forgery is also known as the copy-move forgery (CMF) in which a source region(s) in an image is copied and pasted to different location(s) within the image itself [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The problem of copy-move forgery detection (CMFD) has recently attracted the attention of many researchers, and their works can be partitioned into several categories: keypoint-based, block-based, deep learning-based, and hybrid techniques [5]. Each category has some advantages and International Journal of Intelligent Engineering and Systems, Vol.…”

Section: Introductionmentioning

confidence: 99%

“…16 Both keypoint-based and block-based techniques share the disadvantage of relying on numerous manually created thresholds. On the other hand, deep learning-based techniques are powerfully able to select the suitable thresholds [5]. However, deep learning-based techniques have a number of disadvantages such as its lengthy training process that requires high processing capacity [9].…”

Section: Introductionmentioning

confidence: 99%

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A New Matching Strategy for SIFT Based Copy-Move Forgery Detection

2023

IJIES

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SIFT-based techniques have achieved satisfying performance in detecting copy-move forgery (CMF). Typically, these techniques find the matched regions of an image and re-examine them using a variety of methods to determine whether CMF has occurred or not. However, these techniques have some shortcomings related to how they handle false matches, which usually occur due to image continuity or self-similarity. First, a spatial distance threshold or segmentation-based methods are commonly utilized to handle image continuity. Second, several external methods along with manually created thresholds are utilized to handle image self-similarity. In this paper, we propose a new matching strategy that is resistant to false matches while reducing reliance on external methods and thus avoiding several thresholds. We model the keypoint as a whole region rather than a single point and employ the intersection over union measure to deal with image continuity. To reduce false matches caused by image selfsimilarity, we combine the cross-matching test with a modified distance ratio test. This combination takes into account the ability to detect multiple cloning. Moreover, we utilize a support vector machine to learn the threshold(s) needed to decide if CMF has occurred or not. The proposed methodology is evaluated over three challenging datasets: MICC-F600, Coverage, and MICC-F220. On MICC-F600 dataset, our proposed methodology outperforms other state-of-art techniques and achieves high precision of 99.38%, recall of 97.5%, and 98.42% of F1 score. Additionally, the comparative evaluation using Coverage, and MICC-F220 datasets proved the effectiveness of the proposed methodology to handle a variety of attacks.

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Section: Feature Extraction Phasementioning

confidence: 99%

“…The matching phase aims to find similar keypoints in an image that signify regions where CMF is initially suspected [5]. Two steps make up the matching phase.…”

Section: Matching Phasementioning

confidence: 99%