A Reversible Image Watermarking Scheme with High Contrast Visible Watermarks

[[abstract]]A reversible image watermarking scheme recovers the original host image when extracting the embedded watermarks. General reversible image watermarking scheme embeds invisible watermarks. This paper presents a reversible image watermarking scheme with embedding highly contrast visible watermarks. The host image first segments to non-overlapped blocks. Each block then uses two watermarking schemes including difference-expansion based invisible watermarking and high-contrast visible watermarking to embed one watermark bit into the host image. The difference-expansion based invisible watermarking scheme is adopted for extracting the watermark bit. Some extra information is therefore needed to be recorded. The high contrast visible watermarking scheme embeds significant visible watermarks. Experimental results show that the proposed scheme embeds high contrast visible watermarks and the watermarks can be extracted perfectly.[[notice]]補正完

Difference-Expansion Based Reversible and Visible Image Watermarking Scheme

[[conferencetype]]國內[[conferencedate]]20150817~20150819[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]JiaoSi, Taiwa

Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes

Aradeğerleme hatalarına dayalı bölgesel tabanlı uyarlanabilir tersinir görüntü damgalama

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu tezde aradeğerleme hatalarının bölgesel tabanlı damgalandığı yeni bir tersinir görüntü damgalama yöntemi önerilmiştir. Piksellere ait tahmin değerlerinin elde edilmesinde, pikseller arasındaki uzamsal ilintinin etkin bir şekilde kullanıldığı aradağerleme yöntemi kullanılmıştır. Aradeğerleme işlemi ile elde edilen tahmin değerleri orijinal piksel değerlerinden çıkarılarak piksellere ait hata değerleri elde edilmiş, bu hata değerleri yeni bir yaklaşım ile damga eklemek için kullanılmıştır. Önerilen yöntem; aradağerleme hatalarının histogramında birden fazla hata değerinin damgalandığı bölgesel tabanlı yeni bir tersinir görüntü damgalama yöntemidir. Aradeğerleme hatası histogramı iki bölgeye ayrılarak her bölgeye ait parametrelerin iteratif olarak belirlenmesi ile hedefelenen kapasite değerine daha hassas bir şekilde ulaşılması sağlanmış, böylece damgalanmış görüntü üzerindeki bozunum azaltılmıştır. Bu yeni yöntemde, aradeğerleme hatalarının damgalanmasında daha verimli sonuçların elde edilebilmesi için, uyarlanabilir bir yaklaşım ile hangi piksellerin bir bit hangilerinin birden fazla bit ile damgalanacağına karar verilmektedir. Bu yaklaşım damgalanacak piksellere yer açmak amacıyla ötelenen piksellerin azaltılmasını sağlayan piksel seçme işlemi ile daha da verimli hale getirilmiştir. Önerilen yöntemin, literatürde sıklıkla kullanılan damgalama yöntemlerinden kapasite ve bozunum performansı açısından daha iyi sonuçlar verdiği bilgisayar benzetimleri ile gösterilmiştir.In this thesis, a new reversible image watermarking method is proposed, in which interpolation errors are watermarked in a region based manner. In order to obtain prediction values of the pixels, interpolation method which exploit the spatial correlation of image pixels effectively is used. Prediction error value of a pixel is obtained by calculating differences between the original pixels and the prediction values obtained by the interpolation process. These error values is used to add watermark with a new approach. Proposed method; is a new region-based reversible image watermarking method where more than one error value is watermarked in the histogram of the interpolation errors. The interpolation error histogram is divided into two regions and the parameters related to each region are iteratively determined so that the target capacity value can be reached more precisely. Thereby, distortion on watermarked image is reduced. In this new method, for the sake of efficiency, while interpolation errors are watermarked, it is decided with an adaptive approach which pixels are to be watermarked with one bit, and which pixels are watermarked with more than one bit. This approach has been made even more efficient by pixel selection, which reduces the pixels that are shifted to make room for the watermarked pixels. The superiority of the proposed method in terms of capacity and distortion performance over other methods commonly used in the literature is showed by computer simulations

Learning discrete word embeddings to achieve better interpretability and processing efficiency

L’omniprésente utilisation des plongements de mot dans le traitement des langues naturellesest la preuve de leur utilité et de leur capacité d’adaptation a une multitude de tâches. Ce-pendant, leur nature continue est une importante limite en terme de calculs, de stockage enmémoire et d’interprétation. Dans ce travail de recherche, nous proposons une méthode pourapprendre directement des plongements de mot discrets. Notre modèle est une adaptationd’une nouvelle méthode de recherche pour base de données avec des techniques dernier crien traitement des langues naturelles comme les Transformers et les LSTM. En plus d’obtenirdes plongements nécessitant une fraction des ressources informatiques nécéssaire à leur sto-ckage et leur traitement, nos expérimentations suggèrent fortement que nos représentationsapprennent des unités de bases pour le sens dans l’espace latent qui sont analogues à desmorphèmes. Nous appelons ces unités dessememes, qui, de l’anglaissemantic morphemes,veut dire morphèmes sémantiques. Nous montrons que notre modèle a un grand potentielde généralisation et qu’il produit des représentations latentes montrant de fortes relationssémantiques et conceptuelles entre les mots apparentés.The ubiquitous use of word embeddings in Natural Language Processing is proof of theirusefulness and adaptivity to a multitude of tasks. However, their continuous nature is pro-hibitive in terms of computation, storage and interpretation. In this work, we propose amethod of learning discrete word embeddings directly. The model is an adaptation of anovel database searching method using state of the art natural language processing tech-niques like Transformers and LSTM. On top of obtaining embeddings requiring a fractionof the resources to store and process, our experiments strongly suggest that our representa-tions learn basic units of meaning in latent space akin to lexical morphemes. We call theseunitssememes, i.e., semantic morphemes. We demonstrate that our model has a greatgeneralization potential and outputs representation showing strong semantic and conceptualrelations between related words

Lossless Nibbled Data Embedding Scheme Based on Difference Expansion

[[abstract]]This paper proposes a lossless data embedding scheme of great payload capacity and good image quality, which is based on difference expansion. In this scheme, every pixel in a host image is divided into two nibbles and each nibble pair between two adjacent pixels can be used to hide a secret message. In order to completely recover the host image, the arithmetic coding is adopted based on prediction by partial matching (PPM) model to compress the restored information. This proposed scheme has been successfully applied to different images. According to the experimental results, embedded information can be extracted correctly and quickly from the embedded image. In addition, the proposed scheme can not only hide a large amount of information in a host image without making noticeable distortion, but can also completely restore the host image from the embedded image