ROBUST REVERSIBLE WATERMARKING VIA CLUSTERING AND ENHANCED PIXEL-WISE MASKING
ROBUST REVERSIBLE WATERMARKING VIA CLUSTERING AND ENHANCED PIXEL-WISE MASKING
Robust reversible watermarking (RRW) methods are popular in multimedia for protecting copyright, while preserving intactness of host images and providing robustness against unintentional attacks. However, conventional RRW methods are not readily applicable in practice. That is mainly because: 1) they fail to offer satisfactory reversibility on large-scale image datasets; 2) they have limited robustness in extracting watermarks from the watermarked images destroyed by different unintentional attacks; and 3) some of them suffer from extremely poor invisibility for water marked images. Therefore, it is necessary t o have a framework to address these three problems, and further improve its performance. This project presents a novel pragmatic framework, wavelet-domain statistical quantity histogram shifting and clustering (WSQH-SC). Compared with conventional methods, WSQH-SC ingeniously constructs new watermark embedding and extraction procedures by histogram shifting and clustering, which are important for improving robustness and reducing run-time complexity. Additionally, WSQH-SC includes the property-inspired pixel adjustment to effectively handle overflow and underflow of pixels. This results in satisfactory reversibility and invisibility. Furthermore, to increase its practical applicability, WSQH-SC designs an enhanced pixel-wise masking to balance robustness and invisibility. We perform extensive experiments over natural, medical, and synthetic aperture radar images to show the effectiveness of WSQH-SC by comparing with the histogram rotation-based and histogram distribution constrained methods.
Existing System:
It plays an important role in protecting copyright and content of digital media for sensitive applications, e.g., medical and military images. Although researchers proposed some RW methods for various media, e.g., images audios, videos, and 3-D meshes, they assume the transmission channel is lossless.
The robust RW (RRW) is thus a challenging task. For RRW, the essential objective is to accomplish watermark embedding and extraction in both lossless and lossy environment. As a result, RRW is required to not only recover host images and watermarks without distortion for the lossless channel, but also resist unintentional attacks and extract as many watermarks as possible for the noised channel. Recently, a dozen of RRW methods for digital images have been proposed, which can be classified into two groups: histogram rotation (HR)-based methods and histogram distribution constrained (HDC) methods.
Proposed System:
PIPA: To successfully avoid both overflow and underflow of pixels, we develop PIPA to investigate the intrinsic relationship between wavelet coefficient and pixel changes in order to determine how to duly change wavelet coefficients during the embedding process. By taking the scale and region of wavelet coefficient changes into account, PIPA preprocesses the host images accordingly by adjusting the pixels possible to overflow and underflow into a reliable range before embedding. Finally, the preprocessed host images are used to embed watermarks.
SQH Shifting and Clustering: To better resist unintentional attacks, we build SQH with threshold constraint by deeply studying characteristics of the wavelet coefficients, design the watermark embedding process by bi-directionally shifting SQH, and adopt the k-means clustering algorithm to recover watermarks by creatively modeling the extraction process as a classification problem. Besides from superior robustness, this way simplifies watermark embedding and extraction, and reduces the run-time complexity of the proposed framework.
EPWM: To effectively balance robustness and invisibility, we consider the local sensitivity of human visual system (HVS) in wavelet domain, and design an EPWM to precisely evaluate the just noticeable distortion (JND) thresholds of wavelet coefficients, which thereafter are used to adaptively optimize watermark strength.
Software Requirements:
.Net
Front End – ASP.Net
Language – C#.Net
Back End – SQL Server
Windows XP
Hardware Requirements:
RAM : 512 Mb
Hard Disk : 80 Gb
Processor : Pentium IV
FUTURE WORK:
In future, we will combine the proposed framework with the local feature to further improve robustness. In addition, it is valuable to integrate the merits of sparse representation and probabilistic graphical model into the designing of image watermarking.
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