422 research outputs found
Exact Histogram Specification Optimized for Structural Similarity
An exact histogram specification (EHS) method modifies its input image to
have a specified histogram. Applications of EHS include image (contrast)
enhancement (e.g., by histogram equalization) and histogram watermarking.
Performing EHS on an image, however, reduces its visual quality. Starting from
the output of a generic EHS method, we maximize the structural similarity index
(SSIM) between the original image (before EHS) and the result of EHS
iteratively. Essential in this process is the computationally simple and
accurate formula we derive for SSIM gradient. As it is based on gradient
ascent, the proposed EHS always converges. Experimental results confirm that
while obtaining the histogram exactly as specified, the proposed method
invariably outperforms the existing methods in terms of visual quality of the
result. The computational complexity of the proposed method is shown to be of
the same order as that of the existing methods.
Index terms: histogram modification, histogram equalization, optimization for
perceptual visual quality, structural similarity gradient ascent, histogram
watermarking, contrast enhancement
A Localized Geometric-Distortion Resilient Digital Watermarking Scheme Using Two Kinds of Complementary Feature Points
With the rapid development of digital multimedia and internet techniques in the last few years, more and more digital images are being distributed to an ever-growing number of people for sharing, studying, or other purposes. Sharing images digitally is fast and cost-efficient thus highly desirable. However, most of those digital products are exposed without any protection. Thus, without authorization, such information can be easily transferred, copied, and tampered with by using digital multimedia editing software. Watermarking is a popular resolution to the strong need of copyright protection of digital multimedia. In the image forensics scenario, a digital watermark can be used as a tool to discriminate whether original content is tampered with or not. It is embedded on digital images as an invisible message and is used to demonstrate the proof by the owner. In this thesis, we propose a novel localized geometric-distortion resilient digital watermarking scheme to embed two invisible messages to images. Our proposed scheme utilizes two complementary watermarking techniques, namely, local circular region (LCR)-based techniques and block discrete cosine transform (DCT)-based techniques, to hide two pseudo-random binary sequences in two kinds of regions and extract these two sequences from their individual embedding regions. To this end, we use the histogram and mean statistically independent of the pixel position to embed one watermark in the LCRs, whose centers are the scale invariant feature transform (SIFT) feature points themselves that are robust against various affine transformations and common image processing attacks. This watermarking technique combines the advantages of SIFT feature point extraction, local histogram computing, and blind watermark embedding and extraction in the spatial domain to resist geometric distortions. We also use Watson’s DCT-based visual model to embed the other watermark in several rich textured 80×80 regions not covered by any embedding LCR. This watermarking technique combines the advantages of Harris feature point extraction, triangle tessellation and matching, the human visual system (HVS), the spread spectrum-based blind watermark embedding and extraction. The proposed technique then uses these combined features in a DCT domain to resist common image processing attacks and to reduce the watermark synchronization problem at the same time. These two techniques complement each other and therefore can resist geometric and common image processing attacks robustly. Our proposed watermarking approach is a robust watermarking technique that is capable of resisting geometric attacks, i.e., affine transformation (rotation, scaling, and translation) attacks and other common image processing (e.g., JPEG compression and filtering operations) attacks. It demonstrates more robustness and better performance as compared with some peer systems in the literature
Histogram Based Data Cryptographic Technique with High Level Security
Histogram shifting plays a major role in reversible data hiding technique. By this shifting method the distortion is reduced and the embedding capacity may be increased. This proposed work uses, shifting and embedding function. The pixel elements of the original image are divided into two disjoint groups. The first group is used to carry the secret data and the second group adds some additional information which ensures the reversibility of data. The parameter such as PSNR, embedding capacity and bit rate are used for comparisons of various image
Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication
Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication
Watermarking Based Image Authentication for Secure Color Image Retrieval in Large Scale Image Databases
An important facet of traditional retrieval models is that they retrieve images and videos and consider their content and context reliable. Nevertheless, this consideration is no longer valid since they can be faked for many reasons and at different degrees thanks to powerful multimedia manipulation software. Our goal is to investigate new ways detecting possible fake in social network platforms. In this paper, we propose an approach that assets identification faked images by combining standard content-based image retrieval (CBIR) techniques and watermarking. We have prepared the wartermarked image database of all images using LSB based watermarking. Using gabor features and trained KNN, user is able to retrieve the matching query image. The retrieved image is authenticated by extracting the watermark and matching it again with the test image
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