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DC Field | Value | Language |
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dc.contributor.author | Liu, Fang (劉芳) | |
dc.contributor.author | Dr. Cheng, Lee Ming | |
dc.date.accessioned | 2011-11-17T02:35:54Z | |
dc.date.accessioned | 2017-09-19T09:18:51Z | |
dc.date.accessioned | 2019-02-12T08:40:38Z | - |
dc.date.available | 2011-11-17T02:35:54Z | |
dc.date.available | 2017-09-19T09:18:51Z | |
dc.date.available | 2019-02-12T08:40:38Z | - |
dc.date.issued | 2011-10 | |
dc.identifier.other | EE2011-004 | |
dc.identifier.uri | http://144.214.8.231/handle/2031/6410 | - |
dc.description.abstract | This paper presents a perceptual image hashing algorithm based on wave atom transform, which can authenticate the content preserved images and distinguish the content altered ones in authentication module. Wave atoms are employed since it has significantly sparser expansion and better feature extraction capability than traditional transforms, like wavelet and DCT. In addition, a randomized pixel modulation based on RC4 algorithm is performed to ensure the security. According to the experimental results, the proposed scheme is sensitive to content-alteration attacks with the resiliency to contentpreserving operations, such as JPEG compression, Gaussian noise and contrast enhancement. Moreover, compared with some other image hashing algorithms, the proposed approach also achieves better performance at the aspect of robustness. | |
dc.subject | Image hashing | |
dc.subject | Authentication | |
dc.subject | Wave atom transform | |
dc.subject | Robustness | |
dc.title | Perceptual Image Hashing via Wave Atom Transform | en_US |
dc.type | Article | en_US |
dc.contributor.department | Department of Electronic Engineering | en_US |
dc.description.award | Won the Best Student Paper Award in the 10th International Workshop on Digital-forensics and Watermarking (IWDW 2011) organized by the IWDW 2011 committee. | |
dc.description.supervisor | Dr. Cheng, L. M. | en_US |
Appears in Collections: | Student Works With External Awards |
Files in This Item:
File | Description | Size | Format | |
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award_news.html | 136 B | HTML | View/Open |
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