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Author

Anca Chrisitine Pascu

Bio: Anca Chrisitine Pascu is an academic researcher from University of Western Brittany. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 4, co-authored 7 publications receiving 47 citations. Previous affiliations of Anca Chrisitine Pascu include Centre national de la recherche scientifique.

Papers
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Journal ArticleDOI
TL;DR: The imperceptibility and robustness of the watermarking approach are proven and showing perfectly the detection of the tamper zones are proven.

24 citations

Journal ArticleDOI
TL;DR: The main goal is to propose a hybrid scheme by applying a One Time Pad encryption algorithm and a semi-blind watermarking approach to the host medical image that has a high robustness and a remarkable resistance against geometric, non-geometric and encryption attacks.
Abstract: We present a hybrid and robust encryption and watermarking algorithm based on a new chaotic map key generation method. The chaotic map uses a rule to generate the encryption key using an innovative approach. The main goal is to propose a hybrid scheme by applying a One Time Pad (OTP) encryption algorithm and a semi-blind watermarking approach to the host medical image. The encrypted watermark is embedded in the encrypted host image, directly in the spatial domain. The results obtained show perfect watermark extraction even after applying some attack scenarios. Using this approach, we conclude that our technique has a high robustness and a remarkable resistance against geometric, non-geometric and encryption attacks.

14 citations

Proceedings ArticleDOI
15 Aug 2008
TL;DR: A new approach for modeling color attacks of RGB-color watermarked images using a based interpolation watermarking algorithm and supposed that the attacks are simulated by a scaling of the colors followed by a translation.
Abstract: We present a new approach for modeling color attacks of RGB-color watermarked images. We have used a based interpolation watermarking algorithm and supposed that the attacks are simulated by a scaling of the colors followed by a translation. We give bounds for the extracted watermark depending on the original image, the watermark and the attack. Different attacks like LSB, embedding another watermark, Stirmark have been simulated and the quality of the extracted watermark is discussed in each case.

9 citations

Book ChapterDOI
01 Jan 2018
TL;DR: This chapter introduces intelligent systems based on image watermarking and explores the efficiency of rough set theory in designing robust imageWatermarking with acceptable rate of imperceptibility and robustness against different scenarios of attacks.
Abstract: Computational intelligence involves convenient adaptation and self-organization concepts, theories, and algorithms, which provide appropriate actions for a complex and changing environment. Fuzzy systems, artificial neural networks, and evolutionary computation are the main computational intelligence approaches used in applications. Rough set theory is one of the important fuzzy systems that have a significant role in extracting rough information from vague and uncertain knowledge. It has a pivotal role in many vague problems linked to image processing, fault diagnosis, intelligent recommendation, and intelligent support decision-making. Image authentication and security are one of the essential demands due to the rapid evolution of tele-image processing systems and to the increase of cyberattacks on applications relying on such systems. Designing such image authentication and security systems requires the analysis of digital image characteristics which are, in majority, based on uncertain and vague knowledge. Digital watermarking is a well-known solution for image security and authentication. This chapter introduces intelligent systems based on image watermarking and explores the efficiency of rough set theory in designing robust image watermarking with acceptable rate of imperceptibility and robustness against different scenarios of attacks.

4 citations

Journal ArticleDOI
TL;DR: Application of ZBDD to medical image watermarking will help to take into account not only the complexity and the capacity factors but also the watermark robustness, and the results obtained are very significant and encouraging and will be examined in this paper under several attack scenarios.
Abstract: Watermarking protects legitimate copies of digital multimedia, such as video, audio and images, from unauthorized use. Digital watermarks are used to verify the authenticity, integrity and confidentiality of data to prove the identity of its owners. Watermark generation is one of the most important aspects of watermarking schemes, and should aim to produce as small a watermark as possible a low quantity of data to be embedded in the multimedia to reduce the complexity of computational processes. Although embedding a large amount of watermark data in almost any medium increases the chances of recovering it, this also increases the complexity, which can become impractical for real time applications. In this paper, we focus on the robustness of medical image watermarks and present a means to generate a small watermark. This idea is very innovative in the watermarking field. The proposed approach is based on Zero-Suppressed Binary Decision Diagrams ZBDD. ZBDD has proven its effectiveness in many fields, such as data mining, big data processing, computer networks, etc. Application of ZBDD to medical image watermarking will help us to take into account not only the complexity and the capacity factors but also the watermark robustness. The results obtained are very significant and encouraging and will be examined in this paper under several attack scenarios.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: A new hybrid chaotic map and a different way of using optimization technique to improve the performance of encryption algorithms are proposed, which establishes an excellent randomness performance and sensitivity.
Abstract: This paper proposes a new hybrid chaotic map and a different way of using optimization technique to improve the performance of encryption algorithms. Compared to other chaotic functions, the proposed chaotic map establishes an excellent randomness performance and sensitivity. Based on its Lyapunov exponents and entropy measure, the characteristics of the new mathematical function are better than those of classical maps. We propose a new image cipher based on confusion/diffusion Shannon properties. The substitution phase of the proposed encryption algorithm, which depends on a new optimized substitution box, was carried out by chaotic Jaya optimization algorithm to generate S-boxes according to their nonlinearity score. The goal of the optimization process is to have a bijective matrix with high nonlinearity score. Furthermore, a dynamic key depending on the output of encrypted image is proposed. Security analysis indicates that the proposed encryption scheme can withstand different crypt analytics attacks.

161 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed color image watermarking is not only robust against common image processing operations such as filtering, JPEG compression, histogram equalization, and image blurring, but also robust against the geometrical distortions.

123 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed color image watermarking is not only invisible but also robust against a wide variety of attacks, especially for color attacks and geometric distortions.
Abstract: Based on quaternion Hadamard transform (QHT) and Schur decomposition, a novel color image watermarking scheme is presented. To consider the correlation between different color channels and the significant color information, a new color image processing tool termed as the quaternion Hadamard transform is proposed. Then an efficient method is designed to calculate the QHT of a color image which is represented by quaternion algebra, and the QHT is analyzed for color image watermarking subsequently. With QHT, the host color image is processed in a holistic manner. By use of Schur decomposition, the watermark is embedded into the host color image by modifying the Q matrix. To make the watermarking scheme resistant to geometric attacks, a geometric distortion detection method based upon quaternion Zernike moment is introduced. Thus, all the watermark embedding, the watermark extraction and the geometric distortion parameter estimation employ the color image holistically in the proposed watermarking scheme. By using the detection method, the watermark can be extracted from the geometric distorted color images. Experimental results show that the proposed color image watermarking is not only invisible but also robust against a wide variety of attacks, especially for color attacks and geometric distortions.

110 citations

Posted Content
01 Nov 2017
TL;DR: A novel algorithm based on neutrosophic similarity clustering (NSC) to segment gray level images is proposed and can process both images without noise and noisy images having different levels of noises well.
Abstract: This paper proposed a novel algorithm to segment the objects on images with or without noise.Neutrosophic similarity function is defined to describe the uncertain information on images.A novel objective function is defined using neutrosophic similarity function and the new defined clustering algorithm classifies the pixels on the image into different groups. Segmentation is an important research area in image processing, which has been used to extract objects in images. A variety of algorithms have been proposed in this area. However, these methods perform well on the images without noise, and their results on the noisy images are not good. Neutrosophic set (NS) is a general formal framework to study the neutralities' origin, nature, and scope. It has an inherent ability to handle the indeterminant information. Noise is one kind of indeterminant information on images. Therefore, NS has been successfully applied into image processing algorithms. This paper proposed a novel algorithm based on neutrosophic similarity clustering (NSC) to segment gray level images. We utilize the neutrosophic set in image processing field and define a new similarity function for clustering. At first, an image is represented in the neutrosophic set domain via three membership sets: T, I and F. Then, a neutrosophic similarity function (NSF) is defined and employed in the objective function of the clustering analysis. Finally, the new defined clustering algorithm classifies the pixels on the image into different groups. Experiments have been conducted on a variety of artificial and real images. Several measurements are used to evaluate the proposed method's performance. The experimental results demonstrate that the NSC method segment the images effectively and accurately. It can process both images without noise and noisy images having different levels of noises well. It will be helpful to applications in image processing and computer vision.

64 citations

Journal ArticleDOI
TL;DR: This method generates two image digests from the host image, based on the lifting wavelet and the halftoning technique, which shows the efficiency of TRLH compared to the state of the art methods.

45 citations