Informed embedding is the practice of tailoring each watermarking pattern according to the cover Work in which it is to be embedded. The objective is to attain an optimal trade-off between estimates of perceptual fidelity and robustness. To date, our own studies of informed embedding have been limited to watermarks with very small data payloads. Our purpose in the present paper is to develop a method of informed embedding that is suitable for watermarks with large data payloads. The method we develop employs an estimate of robustness based on the amount of white noise that can be added before a message error becomes likely. We present an iterative, Monte-Carlo algorithm that tries to ensure watermarks are embedded with a specified value of this robustness estimate. This algorithm is tested in an image watermarking system, and is found to successfully embed robust, 129-bit watermarks in 368 × 240 images. Thus the embedder used in those tests was structured as shown inFigure 1. In , we combine the present informed coding method with the informed embedding method of Informed coding is the practice of representing watermark messages with patterns that are dependent on the cover works. This requires the use of a dirty-paper code, in which each message is represented by a large number of alternative vectors. Most previous dirty-paper codes are based on lattice codes, in which each code vector, or pattern, is a point in a regular lattice. While such codes are very efficient to implement, they suffer from inherent weakness against valumetric scaling, such as changes in audio volume or image brightness. In the present paper, we present an alternative to lattice codes that is inherently robust to valumetric scaling. This code is based on a trellis that has been modified so that each bit value may be coded by traversing several alternative arcs. A Viterbi decoder is used in the detector to identify the path with the highest correlation to the input work. Since relative correlation values are unaffected by valumetric scaling, the same message will be detected no matter how the input has been scaled. Several new video and image watermarking proposals are based on Informed Coding and Informed Embedding. However, these systems can be not easily used in fingerprinting schemes because they do not satisfy the marking assumption defined in . In this paper we discuss some guidelines to adapt a watermarking system based on informed coding and informed embedding to a generic fingerprinting code, while keeping up with the marking assumption, that is to say, when as a result of one collusion attack of two users, that have different marks that represent the value 0 in the nth position, we have a pirate mark wich represents the 0 value in this same nth position. This can be achieved modifying the work of Miller, Doër and Cox in . The development explained in this article proves that is possible to trace dishonest users who upload videos with sensitive content to the YouTube service. To achieve tracing these traitor users, fingerprint marks are embedded by a watermarking algorithm into each copy of the video before distributing it. Our experiments show that if the watermarking algorithm is carefully configured and the fingerprints are correctly chosen, the traitor, or a member of a set of traitors who have performed a collusion attack, can be found from a pirate video uploaded to the YouTube service. Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal’s impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher’s actual policy or licence agreement may be applicable. Source.