Rotational copy-move forgery detection using SIFT and region growing strategies

Chen, Chien-Chang; Lu, Wei; Chou, Chung-Hsuan

doi:10.1007/s11042-019-7165-8

Cited by 40 publications

(15 citation statements)

References 27 publications

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“…Prakash et al used accelerated KAZE (AKAZE) and scale invariant feature transform(SIFT) to extract image feature points and combined them into mixed image features, and then used g2NN to match features and locate forged regions [28]. Paul et al used speeded up robust features (SURF) to extract key points of the image, and then used k-nearest neighbor (kNN) training and mapping to realize accurate matches [29]. Chen et al proposed the scheme that used SIFT, moment-invariant calculation, and region growth strategies to detect forged regions [30].…”

Section: Related Workmentioning

confidence: 99%

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Zhou

Tian

Zhou

2021

Front. Comput. Sci.

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Image forgery detection remains a challenging problem. For the most common copy-move forgery detection, the robustness and accuracy of existing methods can still be further improved. To the best of our knowledge, we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network (PCNN) and the self-selected sub-images. Our method has the following steps: First, contour detection is performed on the input color image, and bounding boxes are drawn to frame the contours to form suspected forgery sub-images. Second, by improving PCNN to perform feature extraction of sub-images, the feature invariance of rotation, scaling, noise adding, and so on can be achieved. Finally, the dual feature matching is used to match the features and locate the forgery regions. What's more, the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction, and the improved PCNN can extract image features with high robustness. Through experiments on the standard image forgery datasets CoMoFoD and CASIA, it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method, which is a more efficient image copy-move forgery passive detection method.

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Section: Related Workmentioning

confidence: 99%

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Zhou

Tian

Zhou

2021

Front. Comput. Sci.

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“…In [7], [8], [10], [49], [63] the cloned regions are localized by different methods rather than the common work flow described previously. In [63] to localize the cloned regions; image registration through bi-cubic interpolation is utilized.…”

Section: Forgery Localizationmentioning

confidence: 99%

“…In [63] to localize the cloned regions; image registration through bi-cubic interpolation is utilized. In [8] the cloned regions are localized by region growing technique through Hu's moments. In [49] cloned regions are localized based on multi-scale analysis and a voting process.…”

Section: Forgery Localizationmentioning

confidence: 99%

“…To face the massive increase in image forgeries and its harmful effect, Digital image forensics (DIF) becomes an important area of recent research that verifies images reliability. DIF can be classified into passive and active techniques [7], [8]. Active forensic techniques require special hardware and software to embed authentication information such as digital watermark and digital signature into the image before distribution [7], [9].…”

Section: Introductionmentioning

confidence: 99%

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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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“…This method can achieve accurate positioning, but the accuracy is low when the feature points are not enough. Chen et al [11] proposed the method of using SIFT, moment invariant calculation and regional growth strategy, however, it also takes a long time. In general, the point‐based method has less information to be processed and higher robustness against attack, but the detection effect is not good because of the mismatching and the small number of feature points.…”

Section: Introductionmentioning

confidence: 99%

Image copy‐move forgery detection algorithm based on ORB and novel similarity metric

Tian

Zhou

2020

IET image process

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Rotational copy-move forgery detection using SIFT and region growing strategies

Cited by 40 publications

References 27 publications

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

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

Image copy‐move forgery detection algorithm based on ORB and novel similarity metric

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