Explore the vast range of titles available.

\"This book presents covert, semi-covert and overt techniques for communication over printed media by modifying images, texts or barcodes within the document. Basic and advanced techniques are discussed aimed to modulate information into images, texts and barcodes. Conveying information over printed media can be useful for content authentication, author copyright, information and piracy product deterrent, side information for marketing, among other applications. Practical issues are discussed and experiments are provided to evaluate competitive approaches for hard-copy communication. This book is a useful resource for researchers, practitioners and graduate students in the field of hard-copy communication by providing the fundamentals, basic and advanced techniques as examples of approaches to address the hard-copy media distortions and particularities.\"--Back cover.

In Watermarks in Paper from the South-West of France, 1560-1860 over 200 locally found watermarks are catalogued and described.

\"The nine contributions in The Trade in Papers Marked with non-Latin Characters initiated by Anne Regourd (ed.) approach global history through the paper trade. They cover, in addition to a paper used in 14th C Persia, papers used in Africa (Ethiopia, Nigeria, Tunisia) and Asia (the Ottoman Levant, Mecca, Persia, Russia, and Yemen) during the 19th-20th C. Primarily based on paper examination and quantitative data, the book invites us to treat papers as a source, and provides tools to determine the production of manuscripts in space and time for the area of interest. This methodology offers new insights on the competition between suppliers to the various markets particularly in respect of the emergence of import-export trading companies\"-- Provided by publisher.

Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.

Face swapping is commonly assumed to act locally on the face region, which motivates placing watermarks away from the face to preserve the integrity of the face. We demonstrate that this assumption is violated in practice. Using a region-aware protocol with tunable-strength visible and invisible watermarks and six face-swap families, we quantify both identity transfer and watermark retention on the VGGFace2 dataset. First, edits are non-local—generators alter background statistics and degrade watermarks even far from the face, as measured by background-only PSNR and Pearson correlation relative to a locality-preserving baseline. Second, dependencies between watermark strength, identity transfer, and retention are non-monotonic and architecture-dependent. Methods that better confine edits to the face—typically those employing segmentation-weighted objectives—preserve background signal more reliably than globally trained GAN pipelines. At comparable perceptual distortion, invisible marks tuned to the background retain higher correlation with the background than visible overlays. These findings indicate that classical robustness tests are insufficient alone—watermark evaluation should report region-wise metrics and be strength- and architecture-aware.

This paper presents a secure multiple watermarking method based on discrete wavelet transform (DWT), discrete cosine transforms (DCT) and singular value decomposition (SVD). For identity authentication purpose, the proposed method uses medical image as the image watermark, and the personal and medical record of the patient as the text watermark. In the embedding process, the cover medical image is decomposed up to second level of DWT coefficients. Low frequency band (LL) of the host medical image is transformed by DCT and SVD. The watermark medical image is also transformed by DCT and SVD. The singular value of watermark image is embedded in the singular value of the host image. Furthermore, the text watermark is embedding at the second level of the high frequency band (HH) of the host image. In order to enhance the security of the text watermark, encryption is applied to the ASCII representation of the text watermark before embedding. Results are obtained by varying the gain factor, size of the text watermark, and medical image modalities. Experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks such as JPEG, Gaussian, Salt-and-Pepper, Histogram equalization etc. The performance of the proposed technique is also evaluated by using the benchmark software Checkmark and the technique is found to be robust against the Checkmark attacks such as Collage, Trimmed Mean, Hard and Soft Thresholding, Wavelet Compression, Mid Point, Projective, and Wrap etc.

Embedded watermarking and zero-watermarking, both of which are technologies for copyright protection of digital images, are being actively studied based on their respective advantages and disadvantages. As the prevalence of copyright infringement and the use of irregular-resolution images in game content and media applications increases, there is a growing need for practical and robust image protection techniques. In this paper, we implement discrete wavelet transform (DWT)-discrete cosine transform (DCT)-singular value decomposition (SVD)-based embedded watermarking and hash-based XOR-based zero-watermarking algorithms in Python for about 200 non-standard images with various resolutions and shapes (resolution range: 512 x 512 to 6800 x 4000) and quantitatively compare and analyse their performance. We evaluate the robustness and restoration rate of each watermarking method by applying various attacks such as JPEG compression, blur, Gaussian noise, cropping, and rotation based on peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized correlation (NC) indices. The experimental results show that the embedded method showed fast processing speed and stable quality maintenance performance even in high-resolution images, and zero watermarking had the advantage of not damaging the original image, but was relatively prone to being affected by restoration sensitivity and execution time. While not directly implemented in this study, the findings provide a foundational reference for integrating robust watermarking mechanisms into blockchain and InterPlanetary File System (IPFS) based copyright authentication systems, particularly for protecting high-resolution or irregularly shaped visual assets.

Video watermarking is an important means of video and multimedia copyright protection, but the current watermarking algorithm is difficult to ensure high robustness under various attacks. In this paper, a video watermarking algorithm based on NSCT, pseudo 3D-DCT and NMF has been proposed. Combined with NSCT, 3D-DCT and NMF, the algorithm embeds the encrypted QR code copyright watermark into the NMF base matrix to improve the anti-attack ability of the watermark under the condition of invisibility. The experimental results show that the algorithm ensures the invisibility of the watermark with a high signal-to-noise ratio of the video, and meanwhile has high ability and robustness against common single and combined attacks, such as filtering, noise, compression, shear, rotation and so on. The issue that the video watermarking algorithm has poor resistance to various attacks, especially the shearing attack, has been solved in this paper; thus, it can be used for digital multimedia video copyright protection.

With the deep iteration and innovation of information technology, medical technology is moving towards informatization and intelligence. This has led to a large-scale collection of medical imaging data that carries patient identification information being stored and disseminated over the network. It greatly increases the risk of medical images being leaked, tampered with, and stolen. To address this issue, a zero-watermarking method for encrypted medical images has been proposed based on HC dual chaos and DWT-ResNet-DCT. Firstly, based on the dynamic characteristic coupling of the Henon chaotic map and the Chen chaotic system, an HC dual-chaotic composite system is innovatively designed. And based on the WHT-DCT transform, it proposes a lossless encryption algorithm characterized by initial value sensitivity and a large key space. While ensuring high encryption efficiency, the algorithm achieves “lossless” decryption of medical images. On this basis, this paper proposes a watermarking algorithm based on DWT-ResNet-DCT for encrypted medical images. This algorithm effectively integrates the characteristics of the DWT transform domain and the convolutional neural network ResNet50, enabling accurate extraction of the feature sequence of encrypted medical images. Finally, experiments verify that the algorithm maintains high NC values (greater than 0.8) under traditional attacks, geometric attacks, and combined attacks, demonstrating excellent anti-attack capabilities, especially having good robustness under high-intensity geometric attacks.