The use of digital video offers immense opportunities for creators; however, the ability for anyone to make perfect copies and the ease by which those copies can be distributed also facilitate misuse, illegal copying and distribution ("piracy")

Digital watermarking research has generally focused upon two classes of watermarks, fragile and robust. Robust watermarks are designed to be detected even after attempts are made to remove them. Fragile watermarks are used for authentication purposes and are capable of detecting even minute changes of the watermarked content. Unfortunately, neither type of watermark is ideal when considering "information preserving" transformations (such as compression) which preserve the meaning or expression of the content and "information altering" transformations (such as feature replacement) which change the expression of the content. In this paper we describe a semi-fragile watermark for still images that can detect information altering transformations even after the watermarked content is subjected to information preserving alterations.

A novel MPEG-4 compressed domain video watermarking method is proposed and its performance is studied at video bit rates ranging from 128 to 768 kb/s. The spatial spread-spectrum watermark is embedded directly to compressed MPEG-4 bitstreams by modifying DCT coefficients. A synchronization template combats geometric attacks, such as cropping, scaling, and rotation. The method also features a gain control algorithm that adjusts the embedding strength of the watermark depending on local image characteristics, increasing watermark robustness or, equivalently, reducing the watermark's impact on visual quality. A drift compensator prevents the accumulation of watermark distortion and reduces watermark self-interference due to temporal prediction in inter-coded frames and AC/DC prediction in intra-coded frames. A bit-rate controller maintains the bit rate of the watermarked video within an acceptable limit. The watermark was evaluated and found to be robust against a variety of attacks, including transcoding, scaling, rotation, and noise reduction.Index Terms-MPEG-4, spread spectrum, synchronization template, video watermarking.

Abstract-One of the challenges for blind watermark detection is synchronization. Synchronization is the process of identifying the coordinates of an embedded watermark and is crucial in successful watermark detection. If the detector's input is watermarked but synchronization fails, then the embedded watermark will not be detected. In this paper, temporal synchronization for blind video watermark detection is examined by developing new models for watermark embedding and detection. The structure of the watermark, and specifically its key schedule, dramatically affects the ease of synchronization. The new embedder models the construction of the watermark by using a state machine key generator. The key generator can produce time-invariant, time-independent, and time-periodic key schedules as special cases. The watermark detector uses a queue and a state predictor to perform a search to establish and maintain temporal synchronization. These models are general and can be applied to many symmetric blind video watermarking techniques. It is shown that a watermark without temporal redundancy in its key schedule is vulnerable to attacks such as frame dropping and transposition. Using the models, a watermark more resilient against temporal synchronization attacks is designed by adding temporal redundancy in the watermark construction. Experimental results from an implementation of the models are presented.

Methodologies and tools for watermark evaluation and benchmarking facilitate the development of improved watermarking techniques. In this paper, we want to introduce and discuss the integration of audio watermark evaluation methods into the well-known web service Watermark Evaluation Testbed (WET) [1]. WET is enhanced by using. A special set of audio files with characterized content and a collection of single attacks as well as attack profiles [3] will help to select special audio files and attacks with their attack parameters.