Digital Watermarking as Content Protection Scheme

Nowadays, as the Internet grows rapidly, the copyright laws are not effective anymore, since a lot of copyrighted products (picture, audio, video, document, etc.) are available as digital data. Any unauthorized parties able to produce identical copies of digital data without degrading the original contents and to distribute the copies over the network. This condition has led to a strong demand for reliable and secure distribution of digital data over networks. Such a technique developed to overcome this problem is digital watermarking. Digital watermarking is a process in digital domain, which embeds a watermark into a copyrighted digital data, to protect its value, so that it cannot be used by unauthorized parties. This paper is intended to give an overview on digital watermarking. First, three application fields of watermarking are described and illustrated with some scenarios, namely watermarking for copyright protection, watermarking for copy protection, and watermarking for image authentication. Then watermarking techniques are discussed, starting from the basic watermarking procedure, followed by review of some watermarking techniques. And later, some attacks and obstacles to watermarking are highlighted. In conclusion, digital watermarking technology plays important role in content protection issues. Attacks and obstacles are also had to be faced by this technology. The main obstacle is that there is no standard available for watermarking techniques. Without any specific standard, it is difficult to determine how robust a watermarking technique should be

Improved digital watermarking schemes using DCT and neural techniques

The present thesis investigates the copyright protection by utilizing the digital watermarking of images. The basic spatial domain technique DCT based frequency based technique were studied and simulated. Most recently used Neural Network based DCT Scheme is also studied and simulated. The earlier used Back Propagation Network (BPN) is replaced by Radial Basis Function Neural Network (RBFNN) in the proposed scheme to improve the robustness and overall computation requirements. Since RBFNN requires less number of weights during training, the memory requirement is also less as compared to BPN. Keywords : Digital Watermarking, Back Propagation Network (BPN), Hash Function, Radial Basis Function Neural Network (RBFNN), and Discrete Cosine Transform (DCT). Watermarking can be considered as a special technique of steganography where one message is embedded in another and the two messages are related to each other in some way. The most common examples of watermarking are the presence of specific patterns in currency notes, which are visible only when the note is held to light, and logos in the background of printed text documents. The watermarking techniques prevent forgery and unauthorized replication of physical objects. In digital watermarking a low-energy signal is imperceptibly embedded in another signal. The low-energy signal is called the watermark and it depicts some metadata, like security or rights information about the main signal. The main signal in which the watermark is embedded is referred to as the cover signal since it covers the watermark. In recent years the ease with which perfect copies can be made has lead large-scale unauthorized copying, which is a great concern to the music, film, book and software publishing industries. Because of this concern over copyright issues, a number of technologies are being developed to protect against illegal copying. One of these technologies is the use of digital watermarks. Watermarking embeds an ownership signal directly into the data. In this way, the signal is always present with the data. Analysis Digital watermarking techniques were implemented in the frequency domain using Discrete Cosine Transform (DCT). The DCT transforms a signal or image from the spatial domain to the frequency domain. Also digital watermarking was implemented using Neural Networks such as: 1. Back Propagation Network (BPN) 2. Radial Basis Function Neural Network (RBFNN) Digital watermarking using RBFNN was proposed which improves both security and robustness of the image. It is based on the Cover’s theorem which states that nonlinearly separable patterns can be separated linearly if the pattern is cast nonlinearly into a higher dimensional space. RBFNN contains an input layer, a hidden layer with nonlinear activation functions and an output layer with linear activation functions. Results The following results were obtained:- 1. The DCT based method is more robust than that of the LSB based method in the tested possible attacks. DCT method can achieve the following two goals: The first is that illegal users do not know the location of the embedded watermark in the image. The second is that a legal user can retrieve the embedded watermark from the altered image. 2. The RBFNN network is easier to train than the BPN network. The main advantage of the RBFNN over the BPN is the reduced computational cost in the training stage, while maintaining a good performance of approximation. Also less number of weights are required to be stored or less memory requirements for the verification and testing in a later stage

Improved content based watermarking for images

Due to improvements in imaging technologies and the ease with which digital content can be created and manipulated, there is need for the copyright protection of digital content. It is also essential to have techniques for authentication of the content as well as the owner. To this end, this thesis proposes a robust and transparent scheme of watermarking that exploits the human visual systems’ sensitivity to frequency, along with local image characteristics obtained from the spatial domain, improving upon the content based image watermarking scheme of Kay and Izquierdo. We implement changes in this algorithm without much distortion to the image, while making it possible to extract the watermark by use of correlation. The underlying idea is generating a visual mask based on the human visual systems’ perception of image content. This mask is used to embed a decimal sequence, while keeping its amplitude below the distortion sensitivity of the image pixel. We consider texture, luminance, corner and the edge information in the image to generate a mask that makes the addition of the watermark less perceptible to the human eye. The operation of embedding and extraction of the watermark is done in the frequency domain thereby providing robustness against common frequency-based attacks including image compression and filtering. We use decimal sequences for watermarking instead of pseudo random sequences, providing us with a greater flexibility in the choice of sequence. Weighted Peak Signal to Noise Ratio is used to evaluate the perceptual change between the original and the watermarked image

Robust Identity Perceptual Watermark Against Deepfake Face Swapping

Notwithstanding offering convenience and entertainment to society, Deepfake face swapping has caused critical privacy issues with the rapid development of deep generative models. Due to imperceptible artifacts in high-quality synthetic images, passive detection models against face swapping in recent years usually suffer performance damping regarding the generalizability issue. Therefore, several studies have been attempted to proactively protect the original images against malicious manipulations by inserting invisible signals in advance. However, the existing proactive defense approaches demonstrate unsatisfactory results with respect to visual quality, detection accuracy, and source tracing ability. In this study, we propose the first robust identity perceptual watermarking framework that concurrently performs detection and source tracing against Deepfake face swapping proactively. We assign identity semantics regarding the image contents to the watermarks and devise an unpredictable and unreversible chaotic encryption system to ensure watermark confidentiality. The watermarks are encoded and recovered by jointly training an encoder-decoder framework along with adversarial image manipulations. Extensive experiments demonstrate state-of-the-art performance against Deepfake face swapping under both cross-dataset and cross-manipulation settings.Comment: Submitted for revie