Copy-move forgery detection using combined features and transitive matching

Lin, Cong; Lu, Wei; Huang, Xinchao; Liu, Ke; Sun, Wei; Lin, Huan; Tan, Zhiyuan

doi:10.1007/s11042-018-6922-4

Cited by 51 publications

(9 citation statements)

References 74 publications

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“…So, to limit the scope, in the following analysis we take a historical perspective, but focus especially on recent CNN-based methods. Moreover, we neglect global manipulations, such as histogram equalization or gamma correction [3], [4], which are not necessarily related to a malicious forgeries, as well as methods devoted only to copy-move forgery detection [5], [6], [7], [8].…”

Section: Related Workmentioning

confidence: 99%

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

et al. 2020

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Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods. INDEX TERMS CNN, Digital image forensics, forgery detection.

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Section: Related Workmentioning

confidence: 99%

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

et al. 2020

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“…The SURF and MSER features are extracted from the gray level image separately and then these features are combined to have stronger feature in feature extraction step. The combination of features has been used in various fields in the literature [29], [30] and it has been observed that the rate of true positive rate is higher, and the rate of false positive rate is less than the applications performed with this combined feature compared to the applications performed with the use of these features separately. Therefore, the combination features are used in the proposed method.…”

Section: Proposed Methodsmentioning

confidence: 99%

SURF ve MSER Kombinasyonu ile Kopya Taşı Sahteciliği Algılama

ÇİLTAŞ

Akar

2022

NÖHÜ Müh. Bilim. Derg.

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Because digital images may contain a variety of data, they are regarded as an important source for information sharing. Also, images are widely used as evidence in a variety of real-life cases. The rapid rise in popularity of digital photographs is due to the improvement of technologies. Several software programs have been developed in recent years to modify digital images, such as Photoshop and Corel Photo, however these programs are now being used extensively for forgery. Because of technological advancements, it is difficult for people to recognize faked images with their naked eyes Therefore, in this study, the features used in forgery detection problems are combined to ensure accurate labeling of even forgery images that are difficult to detect. Stronger feature is obtained by combining Speeded-Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER). Considering the experimental results; it has been observed that the use of the proposed method, which is obtained as a result of combining the two methods in copy-move forgery detection problems, is more successful than using the SURF and MSER features separately.

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“…However, some matched keypoints still can't be recognized by the g2NN matching method. Accordingly, the transitive matching is utilized in [50] to enhance the matching relationship.…”

Section: B Feature Matchingmentioning

confidence: 99%

“…Several conventional CMFD techniques utilize the segmentation or the clustering methods to eliminate false matches [24]. The regions/clusters that contain a few matched pairs are discarded [14], [26], [50], [59]. The segmentation and the clustering based algorithms suffer from high time and space complexity [66].…”

Section: E Handling Image Self-similarity and Similar But Genuine Objectsmentioning

confidence: 99%

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

Zedan¹,

Soliman²,

Elsayed³

et al. 2021

IJACSA

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Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. Copy-move forgery detection (CMFD) has currently become an active research topic in the passive/blind image forensics field. There has no doubt that conventional techniques and especially the keypoint based techniques have pushed the CMFD forward in the previous two decades. However, CMFD techniques in general and conventional techniques in particular suffer from several challenges. And thus, increasing approaches are exploiting deep learning for CMFD. In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective.We classify the CMFD techniques into several classifications according to the detection methodology, the detection paradigm, and the detection capability . We discuss the challenges facing the CMFD techniques as well as the ways for solving them. In addition, this survey covers the evaluation metrics and datasets commonly utilized for CMFD. Also, we are debating and proposing certain plans for future research. This survey will be helpful for the researchers' as it master the recent trends of CMFD and outline some future research directions.

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Copy-move forgery detection using combined features and transitive matching

Cited by 51 publications

References 74 publications

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

SURF ve MSER Kombinasyonu ile Kopya Taşı Sahteciliği Algılama

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

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