Image Recapture Detection Using Multiple Features

Ke, Yinglin; Shan, Qingqing; Fan, Qiang; Min, Weidong

doi:10.14257/ijmue.2013.8.5.08

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…Ke et al, [2] proposed an image recapture detection method based on multiple feature descriptors. It uses combinations of low dimensional features including texture feature, noise feature, difference of histogram feature and colour feature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As suggested by Ke et al,[2], the whole dataset is partitioned into training and testing images as shown in Table 3 and measured the performance using accuracy. Table 4 shows the recognition rate of proposed method using different training and testing samples.…”

Section: Experiments Imentioning

confidence: 99%

“…In recent years, considerable amount of researches are conducting for image recaptured detection to restore the trustworthiness of digital images [1], [2], [3]. Restoring the intrinsic image regularities automatically and removing some common tampering anomalies automatically are several possibilities of using the image recapturing process.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Physical Based Features for Recognition of Recaptured Images

Samaraweera¹,

Mayurathan²

2016

IJCSIT

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Section: Literature Reviewmentioning

confidence: 99%

Section: Experiments Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Physical Based Features for Recognition of Recaptured Images

Samaraweera¹,

Mayurathan²

2016

IJCSIT

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“…The performance of proposed method is compared with the state-of-the-art methods. As suggested by Ke et al, [2], the whole dataset is partitioned into training and testing images as shown in Table 3 and measured the performance using accuracy. Table 4 shows the recognition rate of proposed method using different training and testing samples.…”

Section: Experiments Imentioning

confidence: 99%

“…In recent years, considerable amount of researches are conducting for image recaptured detection to restore the trustworthiness of digital images [1], [2], [3]. Using the image recapturing process it is possible to restore the intrinsic image regularities automatically and to remove some common tampering anomalies automatically.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Recaptured Images Using Physical Based Features

S.A.A.H.¹,

Mayurathan²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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With the development of multimedia technology and digital devices, it is very simple and easier to recapture a high quality images from LCD screens. In authentication, the use of such recaptured images can be very dangerous. So, it is very important to recognize the recaptured images in order to increase authenticity. Image recapture detection (IRD) is to distinguish realscene images from the recaptured ones. An image recapture detection method based on set of physical based features is proposed in this paper, which uses combination of low-level features including texture, HSV colour and blurriness. Twenty six dimensions of features are extracted to train a support vector machine classifier with linear kernel. The experimental results show that the proposed method is efficient with good recognition rate of distinguishing real scene images from the recaptured ones. The proposed method also possesses low dimensional features compared to the state-of-the-art recaptured methods.

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