715 research outputs found
Image Watermaking With Biometric Data For Copyright Protection
In this paper, we deal with the proof of ownership or legitimate usage of a
digital content, such as an image, in order to tackle the illegitimate copy.
The proposed scheme based on the combination of the watermark-ing and
cancelable biometrics does not require a trusted third party, all the exchanges
are between the provider and the customer. The use of cancelable biometrics
permits to provide a privacy compliant proof of identity. We illustrate the
robustness of this method against intentional and unintentional attacks of the
watermarked content
Using SVD and DWT Based Steganography to Enhance the Security of Watermarked Fingerprint Images
Watermarking is the process of embedding information into a carrier file for the protection of ownership/copyright of digital media, whilst steganography is the art of hiding information. This paper presents, a hybrid steganographic watermarking algorithm based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transforms in order to enhance the security of digital fingerprint images. A facial watermark is embedded into fingerprint image using a method of singular value replacement. First, the DWT is used to decompose the fingerprint image from the spatial domain to the frequency domain and then the facial watermark is embedded in singular values (SV’s) obtained by application of SVD. In addition, the original fingerprint image is not required to extract the watermark. Experimental results provided demonstrate the methods robustness to image degradation and common signal processing attacks, such as histogram and filtering, noise addition, JPEG and JPEG2000 compression with various levels of quality
Contextual biometric watermarking of fingerprint images
This research presents contextual digital watermarking techniques using face and demographic text data as multiple watermarks for protecting the evidentiary integrity of fingerprint image. The proposed techniques embed the watermarks into selected regions of fingerprint image in MDCT and DWT domains. A general image watermarking algorithm is developed to investigate the application of MDCT in the elimination of blocking artifacts. The application of MDCT has improved the performance of the watermarking technique compared to DCT. Experimental results show that modifications to fingerprint image are visually imperceptible and maintain the minutiae detail. The integrity of the fingerprint image is verified through high matching score obtained from the AFIS system. There is also a high degree of correlation between the embedded and extracted watermarks. The degree of similarity is computed using pixel-based metrics and human visual system metrics. It is useful for personal identification and establishing digital chain of custody. The results also show that the proposed watermarking technique is resilient to common image modifications that occur during electronic fingerprint transmission
Color-decoupled photo response non-uniformity for digital image forensics
The last few years have seen the use of photo response non-uniformity noise (PRNU), a unique fingerprint of imaging sensors, in various digital forensic applications such as source device identification, content integrity verification and authentication. However, the use of a colour filter array for capturing only one of the three colour components per pixel introduces colour interpolation noise, while the existing methods for extracting PRNU provide no effective means for addressing this issue. Because the artificial colours obtained through the colour interpolation process is not directly acquired from the scene by physical hardware, we expect that the PRNU extracted from the physical components, which are free from interpolation noise, should be more reliable than that from the artificial channels, which carry interpolation noise. Based on this assumption we propose a Couple-Decoupled PRNU (CD-PRNU) extraction method, which first decomposes each colour channel into 4 sub-images and then extracts the PRNU noise from each sub-image. The PRNU noise patterns of the sub-images are then assembled to get the CD-PRNU. This new method can prevent the interpolation noise from propagating into the physical components, thus improving the accuracy of device identification and image content integrity verification
Camera-based Image Forgery Localization using Convolutional Neural Networks
Camera fingerprints are precious tools for a number of image forensics tasks.
A well-known example is the photo response non-uniformity (PRNU) noise pattern,
a powerful device fingerprint. Here, to address the image forgery localization
problem, we rely on noiseprint, a recently proposed CNN-based camera model
fingerprint. The CNN is trained to minimize the distance between same-model
patches, and maximize the distance otherwise. As a result, the noiseprint
accounts for model-related artifacts just like the PRNU accounts for
device-related non-uniformities. However, unlike the PRNU, it is only mildly
affected by residuals of high-level scene content. The experiments show that
the proposed noiseprint-based forgery localization method improves over the
PRNU-based reference
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