A Joint Biometrics and Watermarking Based Framework for Fingerprinting, Copyright Protection, Proof of Ownership, and Security Applications

Ahmad, S. S.; Lu, Zhe‐Ming

doi:10.1109/cisw.2007.4425586

Cited by 23 publications

(2 citation statements)

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“…Each watermark bit is embedded in various frequency bands, and the information of the watermark bits are spread throughout large spatial regions. Ahmad and Lu [19] considered iris as the watermark. The features of iris are extracted using one-dimensional Gabor filters and then get encoded into bits using Daugman's four-level phase quantization.…”

Section: Related Workmentioning

confidence: 99%

A Novel Image Theft Identification Using Biometric Feature

Fernandez

Nithyanandam

Chavali³

et al. 2017

Asian J Pharm Clin Res

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In today's internet world, all the data are represented and stored in digital form. Almost any entity in this world can be represented digitally, ranging from simple text to complex multimedia work. Now, the challenge is to claim the ownership and prevent theft of one's own digital data. Multimedia theft has driven the attention of many stakeholders who spend huge money and precious time in creating or making such valuable digital data. Among all the multimedia entities, image files are more vulnerable for theft since it is the basic component of any visuals. The notion of this research work is to propose an image theft detection model which will determine whether partial theft or complete theft of an image has occurred or not. A biometric feature, i.e., fingerprint of the owner is embedded on the digital image at a micro level, such that even a very small portion of image theft can be determined, and the ownership of the image can be claimed by the owner. This research is limited to the spatial domain, i.e. raw image. Assessment metrics of the results shows that embedding the biometric feature on an image does not distort the image quality and its artifacts.

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Section: Related Workmentioning

confidence: 99%

A Novel Image Theft Identification Using Biometric Feature

Fernandez

Nithyanandam

Chavali³

et al. 2017

Asian J Pharm Clin Res

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“…These watermarking algorithms should thus be highly robust so that any degradation of the watermarked image would not degrade hidden data utility (the main source of authentication). An example of a biometric authentication scheme using fingerprints only is proposed in . Also, Zebbiche et al , proposes an application of a wavelet‐based watermarking method to hide fingerprint minutiae data in fingerprint images.…”

Section: Related Workmentioning

confidence: 99%

Biometric watermarks based on face recognition methods for authentication of digital images

Wójtowicz

Ogiela

2014

Security Comm Networks

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Because making digital images secure runs into the substantial challenge of owner authentication, many security schemes based on cryptography, steganography and watermarking technology include biometric recognition methods. To follow on these studies, this paper describes a combination of facial images with watermarking technology to automatically authenticate digital images owners/users. In the proposed methodology, biometric face recognition methods such as principal component analysis and eigenfeature regularization and extraction produce vectorial representations of facial images. These vectors are used as copyright watermarks, in a few common watermarking schemes, and are tested for identification purposes after they are extracted. Initially, watermarking algorithms are studied with some arbitrary cover image, and also the most robust algorithm is tested for different cover images of particular subjects. The strength of this paper is finding relationships between the original and extracted biometric data using neural networks instead of the most common, simple measures such as correlation coefficients or distance metrics. The NN subject identification is performed directly, as there is no need to reconstruct facial images after the watermarks are extracted, compute templates for particular subjects, or seek a suitable distance metric. What is more, the presented study includes a performance comparison of two machine learning methods, frequently used for face recognition, and of a few popular watermarking algorithms. Very promising identification results were obtained in many considered experiments, even those involving attacks on watermarked images. and watermarking [7,[15][16][17][18] are provided, because they now represent mainstream security technologies. This paper proposes a security model enabling the authentication of digital image owners using the watermarking

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