Efficient Image Copy Detection Using Multiscale Fingerprints

Ling, Hefei; Cheng, Haiying; Ma, Qiang; Zou, Fang; Wan, Yan

doi:10.1109/mmul.2011.75

Cited by 39 publications

(23 citation statements)

References 14 publications

(18 reference statements)

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“…We randomly select 1000 images from the image dataset to be query images, and the rest are treated as non-copies. Using Stirmark, every query image is modified to generate 100 copies in the way of 9 . After that, the 100,000 copies are inserted into the image dataset for query image to search their copy.…”

Section: Experiments Settingmentioning

confidence: 99%

Feature Fusion based Hashing for Large Scale Image Copy Detection

Yan¹,

Ling²,

Ye³

et al. 2015

IJCIS

Self Cite

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Currently, researches on content based image copy detection mainly focus on robust feature extraction.However, most of existing approaches use only a single feature to represent an image for copy detection, which is often insufficient to characterize the image content. Besides, with the exponential growth of online images, it's urgent to explore a way of tackling the problem of large scale. In this paper, we propose a feature fusion based hashing method which effectively utilize the correlation between two feature models and efficiently accomplish large scale image copy detection. To accurately map images into the Hamming space, our hashing method not only preserves the local structure of individual feature but also globally consider the local structures for all the features to learn a group of hash functions. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.

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Section: Experiments Settingmentioning

confidence: 99%

Feature Fusion based Hashing for Large Scale Image Copy Detection

Yan¹,

Ling²,

Ye³

et al. 2015

IJCIS

Self Cite

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“…Scale invariant feature transform (SIFT) was also employed for image forgery detection [11,12,13]. The methods based on SIFT are robust against scaling and rotation post-processing.…”

Section: Realated Workmentioning

confidence: 99%

Comparison between WLD and LBP descriptors for non-intrusive image forgery detection

Hussain¹,

Saleh

Aboalsamh

et al. 2014

2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings

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Abstract-Due to the availability of easy-to-use and powerful image editing tools, the authentication of digital images cannot be taken for granted and it gives rise to non-intrusive forgery detection problem because all imaging devices do not embed watermark. We investigated the detection of copy-move and splicing, the two harmful types of image forgery, using textural properties of images. Tampering distorts the texture micropatterns in an image and texture descriptors can be employed to detect tampering. We did comparative study to examine the effect of two state-of-the-art best texture descriptors: Multiscale Local Binary Pattern (Multi-LBP) and Multiscale Weber Law Descriptor (Multi-WLD). Multiscale texture descriptors extracted from the chrominance components of an image are passed to Support Vector Machine (SVM) to identify it as authentic or forged. The performance comparison reveals that Multi-WLD performs better than Multi-LBP in detecting copymove and splicing forgeries. Multi-WLD also outperforms stateof-the-art passive forgery detection techniques.

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“…The method is robust against JPEG compression and a certain degree of rotation and scaling. Scale invariant feature transform (SIFT) based forgery detection methods are proposed in [9], [10], [11]. They are quite robust against rotation and scaling postprocessing.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

Hussain

Qasem

Bebis

et al. 2015

Int. J. Artif. Intell. Tools

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Abstract. In this paper, a detailed evaluation of multi-scale Weber local descriptors (WLD) based image forgery detection method is presented. Multiscale WLD extracts the features from chrominance components of an image, which usually encode the tampering information that escapes the human eyes. The WLD incorporates differential excitation and gradient orientation of a center pixel around a neighborhood. In the multi-scale WLD, three different neighborhoods are chosen. A support vector machine is used for classification purpose. The experiments are conducted on three image databases, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results show that the accuracy rate of the proposed method are 94.19% for CASIA v1.0, 96.61% for CASIA v2.0, and 94.17% for Columbia dataset. These accuracies are significantly higher than those obtained by some state-of-the-art methods.

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Efficient Image Copy Detection Using Multiscale Fingerprints

Cited by 39 publications

References 14 publications

Feature Fusion based Hashing for Large Scale Image Copy Detection

Feature Fusion based Hashing for Large Scale Image Copy Detection

Comparison between WLD and LBP descriptors for non-intrusive image forgery detection

Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

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