Region duplication detection based on hybrid feature and evaluative clustering

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

doi:10.1007/s11042-019-7342-9

Cited by 29 publications

(13 citation statements)

References 74 publications

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“…As a result, the generalized nearest neighbor (g2NN) matching method is proposed to work when the region is cloned several times. The g2NN matching method iterates the 2NN test until the distance ratio become greater than the specified threshold [4], [5], [9], [14], [25], [26]. However, some matched keypoints still can't be recognized by the g2NN matching method.…”

Section: B Feature Matchingmentioning

confidence: 99%

“…Keypoint based CMFD techniques fail to detect the forgery if insufficient keypoints are identified that results in insufficient correct matched pairs, and that is the situation when dealing with smooth or small cloned regions or when the input image is of low resolution [4], [68]. One way to extract more keypoints is to utilize hybrid/multiple detectors such as in [5], [9], [26]. Other works such as [15], [25] applied the keypoints detectors on the opponent color space rather than the intensity channel to get an adequate number of keypoints.…”

Section: Dealing With Small or Smooth Cloned Regionsmentioning

confidence: 99%

“…Among the image forgery types, CMF is the most common and difficult forgery type . In CMF or image cloning, a part of an image (an authentic source region) is replicated and then pasted to another part of the same image (the forged region) [1], [4] in order to remove unwanted object or replicating desirable object [5]- [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: B Feature Matchingmentioning

confidence: 99%

Section: Dealing With Small or Smooth Cloned Regionsmentioning

confidence: 99%

See 1 more Smart Citation

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

Zedan¹,

Soliman²,

Elsayed³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…×(cos(lθ)−i sin(lθ))rdrdθ. (18) PCT and PST are defined on unit circle as well. |M C nl | and |M S nl | are rotation invariant.…”

Section: B Polar Cosine Transform and Polar Sine Transform (Pct And Pst)mentioning

confidence: 99%

“…Al-asady and Al-amery [17] obtain accurate features for human action detection. Lin et al [18] and Liu et al [19] propose region duplication detection scheme for feature point mapping.…”

Section: Introductionmentioning

confidence: 99%

GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Yang

Tang

et al. 2020

IEEE Access

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Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature, which can improve the effect in Intelligent Transportation System (ITS) applications. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). PHTs can extract orthogonal and rotation invariant features and demonstrated superior performance in various image processing and computer vision applications. For real time systems and large multimedia databases, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this study presents GPU based PHTs. Proposed methods are based on mathematical properties of PHTs and optimization techniques of GPU. Optimal parameter selections for GPU execution are also discussed. In our experiments, proposed methods are over 1800 times faster. INDEX TERMS GPU, Polar harmonic transform, feature extraction, intelligent transportation system.

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Near duplicate detection of images with area and proposed pixel‐based feature extraction

Governor

Ramanujam²,

Mana

et al. 2022

Concurrency and Computation

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Efficient and accurate near-duplicate recognition is the trendy research area. Identification of invalid near-duplicate images offers a wide range of applications, including digital picture forensics, web-scale retrieval, and, social media analysis. This article intends to introduce a novel near duplicate detection model of images that consists of two stages such as (i) feature extraction and (ii) similarity computation. Originally, the image database is subjected to extracting the features, in which the area-based features and pixel-based features are extracted. Here, the area-based feature extraction includes the contrast context histogram (CCH-descriptors) and improved weighted bag of visual word (w-BovW) features; the pixel-based feature extraction includes the texture features like the proposed local vector pattern. Once the query image is given as the input, it is subjected to the feature extraction stage. Then, the feature vector database and the extracted features of query images are evaluated under similarity computation via improved Jaccard similarity evaluation. Thus, the near duplicate detection of the image is obtained in an effective manner. K E Y W O R D Sfeature extraction, feature vector database, local vector pattern, near duplicate detection, similarity computation INTRODUCTIONNowadays, digital contents are widespread and are redistributed easily, it may be lawful or unlawful. For instance, travelers, who travel to many places, take pictures, write travelogues and post them on the internet, these pictures are taken with or without viewpoint variation and that may be the shot from the same place. These images posted on the internet can be modified by other web users and they may repost them as their version.This generates a near-duplicate image. The near duplicate images are modified with the descriptions of normal images created through compression, geometric alterations, content augmentation, blurring, noise pollution, retaining and cutting out parts, and other techniques. The presence of those near-duplicate images causes wastage of storage, computing, and transmission resources, and it also affects the performance of the search engines critically, as the user has to search among the huge amount of near-duplicate images, for finding the image they want. Near-duplication of images 1,2 leads to wastage of resources in the network and it might be an indication of illegal transactions like image copyright violation. As a result, in web content security and image management, effective and efficient near-duplicate detection (NDD) of the picture is critical. Extracting useful features of an image to increase recognition accuracy is one of the key problems in near duplication image recognition. 3,4 Another issue is to increase the detection efficiency, which is difficult while considering the large image databases.The development of efficient and accurate NDD algorithms for various types of material such as video, image, text, and speech is a hot issue in research. 5,6 Near duplicates plays a crucial...

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Region duplication detection based on hybrid feature and evaluative clustering

Cited by 29 publications

References 74 publications

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

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

GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Near duplicate detection of images with area and proposed pixel‐based feature extraction

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