Effects of Spatial Domain Image Watermarking on Types of Printers and Printing Papers

2015 ◽  
Vol 781 ◽  
pp. 564-567
Author(s):  
Thumrongrat Amornraksa ◽  
Kharittha Thongkor
Keyword(s):  
Image Quality ◽  
Image Registration ◽  
Projective Transformation ◽  
Image Watermarking ◽  
Spatial Domain ◽  
Different Types ◽  
Image Pixels ◽  
Perspective Distortions ◽  
Printing Papers

This paper presents the performance investigations of the spatial domain image watermarking for camera-captured images on different types of printers and printable materials. We examine the effects of our previous watermarking method based on the modification of image pixels on three types of printers, i.e. inkjet, laser and photo printers, and four different types of printing papers, i.e. uncoated, matte, glossy and semi-glossy papers. In the experiments, the DSLR camera is used as tool to capture the printed watermarked images, while the image registration technique based on projective transformation is used to diminish the RST and perspective distortions in the captured image. The performances in terms of extracted watermark accuracy at equivalent watermarked image quality on different types of printers and printing papers are measured and compared.

Gaussian Filter Based Image Watermarking

2015 ◽  
Vol 781 ◽  
pp. 519-522
Author(s):  
Kharittha Thongkor ◽  
Thumrongrat Amornraksa
Keyword(s):  
Gaussian Distribution ◽  
Image Watermarking ◽  
Local Area ◽  
Spatial Domain ◽  
Previous Method ◽  
Gaussian Filter ◽  
Image Pixels

We present a spatial domain image watermarking method based on a Gaussian filter: it assumes that, in a watermarking method based on the modifying blue image pixels, if the watermark to be embedded within a local area is controlled by a Gaussian distribution, a Gaussian filter based watermark extraction will be the most fitted method used to obtain the highest accurate version of extracted watermark. Experiments show the accuracy of the extracted watermark in terms of NC was notably improved from our previous method. The robustness against attacks was also improved.

scholarly journals Image Watermarking Based on IWT and Parity Bit Checking

2021 ◽  
pp. 2726-2739
Author(s):  
Jalal H. Awad ◽  
Balsam D. Majeed
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Floating Point ◽  
Integer Coefficient ◽  
Authentication Mechanism ◽  
Image Pixels

     Various document types play an influential role in a lot of our lives activities today; hence preserving their integrity is an important matter. Such documents have various forms, including texts, videos, sounds, and images.  The latter types' authentication will be our concern here in this paper. Images can be handled spatially by doing the proper modification directly on their pixel values or spectrally through conducting some adjustments to some of the addressed coefficients. Due to spectral (frequency) domain flexibility in handling data, the domain coefficients are utilized for the watermark embedding purpose. The integer wavelet transform (IWT), which is a wavelet transform based on the lifting scheme, is adopted in this paper in order to provide a direct way for converting image pixels' integer values to integer coefficient values rather than floating point coefficients that could be produced by the traditional wavelet transform. This direct relation can enhance the processed image quality due to avoiding the rounding operations on the floating point coefficients. The well-known parity bit approach is also utilized in this paper as an authentication mechanism, where 3 secret parity bits are used for each block in an image which is divided into non-overlapped blocks in order to enforce a form of fragile watermark approach. Thus, any alteration in the block pixels could cause the adopted (even) parity to be violated. The fragile watermarking is achieved through the modification of least significant bits ((LSBs) of certain frequency coefficients' according to the even parity condition. In spite of this image watermarking operation, the proposed method is efficient. In order to prove the efficiency of our proposed method, it was tested against standard images using measurements like peak signal to noise ratio (PSNR) and structural similarity index (SSIM).  Experiments showed promising results; the method preserves high image quality (PSNR≈ 44.4367dB, SSIM≈ 0.9956) and good tamper detection capability.

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals Estimation of contrast-to-noise ratio in CT and CBCT images with varying scan settings in presence of different implant materials

2019 ◽  
Vol 48 (8) ◽  
pp. 20190139
Author(s):  
Emine Şebnem Kursun-Cakmak ◽  
Husniye Demirturk Kocasarac ◽  
Seval Bayrak ◽  
Gülbahar Ustaoğlu ◽  
Marcel Noujeim
Keyword(s):  
Image Quality ◽  
Imaging Modality ◽  
Cone Beam Ct ◽  
Medical Systems ◽  
Plastic Container ◽  
Implant Materials ◽  
Different Types ◽  
Noise Ratio ◽  
Titanium Grade ◽  
Imagej Software

Objectives: To assess the contrast-to-noise ratio (CNR) of four different types of dental implant materials in CT and cone beam CT (CBCT) images with varying scan settings. Methods: Four different types of implants: zirconium (Zr), titanium (Ti) Grade 4 and 5 and titanium–zirconium (Ti–ZrO2) alloy were placed in a 3% gelatin phantom in a cylindrical plastic container and scanned with two different CT machines (GE Medical systems and Toshiba Medical Systems) and one CBCT machine (I-CAT, Imaging Sciences International) with different voxel sizes of 0.2, 0.25, 0.3 and 0.4 mm. Images were analyzed using ImageJ software with the purpose of estimating the CNR. Results: The CNR obtained from images acquired with CT was lower than the CBCT with all voxel sizes tested. 0.3 and 0.4 mm voxel sizes exhibited the highest CNR (p < 0.05) that gives the best image quality. Among the implant materials tested, titanium Grade 5 has the highest levels of CNR while Zirconium has the lowest (p < 0.05). Conclusions: The optimum protocol for radiographic follow-up in areas near implants on the I-CAT is low-resolution settings (0.3 and 0.4 mm voxel sizes) which gave the highest CNR thus image quality. In presence of Zr implants, an alternative imaging modality (i.e., MRI) may be considered to avoid low-quality images.

Blind Digital Image Watermarking Algorithm Based on the Chain Code

2012 ◽  
Vol 546-547 ◽  
pp. 410-415
Author(s):  
Chun Ge Tang ◽  
Tie Sheng Fan ◽  
Lei Liu ◽  
Zhi Hui Li
Keyword(s):  
Image Quality ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Watermarking ◽  
Boundary Representation ◽  
Experimental Results ◽  
Digital Image Watermarking ◽  
Original Image ◽  
Chain Code ◽  
Decision Method

A new blind digital watermarking algorithm based on the chain code is proposed. The chain code is obtained by the characteristics of the original image -the edge contour. The feather can reflect the overall correlation of the vector image, and chain code expression can significantly reduce the boundary representation of the amount of data required. For the watermarking embedding, the original vector image is divided into sub-block images, and two bits of the watermarking information are embedded into sub-block images repeatedly by quantization. For watermarking extracting, the majority decision method is employed to determine the size of the extracted watermark. Experimental results show that the image quality is not significantly lowered after watermarking. The algorithm can resist the basic conventional attacks and has good robustness on the shear attacks.

Fast and robust image watermarking method in the spatial domain

2020 ◽  
Author(s):  
Zihan Yuan ◽  
Qingtang Su ◽  
Decheng Liu ◽  
Xueting Zhang ◽  
Tao Yao
Keyword(s):  
Image Watermarking ◽  
Spatial Domain ◽  
Robust Image

scholarly journals Image Registration Algorithm Based on Convolutional Neural Network and Local Homography Transformation

2020 ◽  
Vol 10 (3) ◽  
pp. 732 ◽  
Author(s):  
Yuanwei Wang ◽  
Mei Yu ◽  
Gangyi Jiang ◽  
Zhiyong Pan ◽  
Jiqiang Lin
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Estimation Algorithm ◽  
Matrix Estimation ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Different Types ◽  
Homography Transformation ◽  
Traditional Image

In order to overcome the poor robustness of traditional image registration algorithms in illuminating and solving the problem of low accuracy of a learning-based image homography matrix estimation algorithm, an image registration algorithm based on convolutional neural network (CNN) and local homography transformation is proposed. Firstly, to ensure the diversity of samples, a sample and label generation method based on moving direct linear transformation (MDLT) is designed. The generated samples and labels can effectively reflect the local characteristics of images and are suitable for training the CNN model with which multiple pairs of local matching points between two images to be registered can be calculated. Then, the local homography matrices between the two images are estimated by using the MDLT and finally the image registration can be realized. The experimental results show that the proposed image registration algorithm achieves higher accuracy than other commonly used algorithms such as the SIFT, ORB, ECC, and APAP algorithms, as well as another two learning-based algorithms, and it has good robustness for different types of illumination imaging.

Hardware Implementation of a Visual Image Watermarking Scheme Using Qubit/Quantum Computation Through Reversible Methodology

2018 ◽  
pp. 95-140
Author(s):  
Subhrajit Sinha Roy ◽  
Abhishek Basu ◽  
Avik Chattopadhyay
Keyword(s):  
Power Dissipation ◽  
Hardware Implementation ◽  
Image Watermarking ◽  
Visual Image ◽  
Reversible Logic ◽  
Process Data ◽  
Replacement Technique ◽  
Lsb Replacement ◽  
Image Pixels ◽  
Watermarking Scheme

In this chapter, hardware implementation of an LSB replacement-based digital image watermarking algorithm is introduced. The proposed scheme is developed in spatial domain. In this watermarking process, data or watermark is implanted into the cover image pixels through an adaptive last significant bit (LSB) replacement technique. The real-time execution of the watermarking logic is developed here using reversible logic. Utilization of reversible logic reduces the power dissipation by means of no information loss. The lesser power dissipation enables a faster operation as well as holds up Moore's law. The experimental results confirm that the proposed scheme offers high imperceptibility with a justified robustness.

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