The class of joint decoder in fingerprinting codes is of utmost importance in theoretical papers to establish the concept of fingerprint capacity. However, no implementation supporting a large user base is known to date. This paper presents an iterative decoder which is the first attempt toward practical large-scale joint decoding. The discriminative feature of the scores benefits on one hand from the side-information of previously found users, and on the other hand, from recently introduced universal linear decoders for compound channels. Neither the code construction nor the decoder makes assumptions about the collusion size and strategy, provided it is a memoryless and fair attack. The extension to incorporate soft outputs from the watermarking layer is straightforward. An extensive experimental work benchmarks the very good performance and offers a clear comparison with previous state-of-the-art decoders.

This paper links two a priori different topics, group testing and traitor tracing. Group testing, as an instantiation of a compressed sensing problem over binary data, is indeed easier than traitor tracing because the mixing model is far simpler. State-of-the-art algorithms for traitor tracing, including the celebrated probabilistic Tardos code, are applied to the group testing problem. They yield better than or competitive performance when compared to state-of-the-art algorithms.

In this paper, we introduce a Goodness-of-Fit test for the Multivariate Exponential Power (MEP) distribution, a multivariate extension of the Generalized Gaussian, which has recently gained considerable interest as a model for wavelet coefficients in the context of color image retrieval and spread-spectrum watermarking. We present a size and power study of this test and show Goodness-of-Fit results for wavelet coefficients of natural and texture images from various popular databases.

We propose a blind iterative single Tardos decoder for traitor tracing designed to catch as many colluders as possible while controlling the probability of accusing an innocent. The key idea is that users accused in the previous iterations are used as side-information to build a more discriminative test. A fast implementation supporting millions of users is presented and compared with two recent fingerprinting codes.

This article aims at lightweight, blind detection of additive spread-spectrum watermarks in the DWT domain. We focus on two host signal noise models and two types of hypothesis tests for watermark detection. As a crucial point of our work we take a closer look at the computational requirements of watermark detectors. This involves the computation of the detection response, parameter estimation and threshold selection. We show that by switching to approximate host signal parameter estimates or even fixed parameter settings we achieve a remarkable improvement in runtime performance without sacrificing detection performance. Our experimental results on a large number of images confirm the assumption that there is not necessarily a tradeoff between computation time and detection performance.

In this paper, we investigate a novel joint statistical model for subband coefficient magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian framework for content-based image retrieval. The joint model allows to capture the association among transform coefficients of the same decomposition scale and different color channels. It further facilitates to incorporate recent research work on modeling marginal coefficient distributions. We demonstrate the applicability of the novel model in the context of color texture retrieval on four texture image databases and compare retrieval performance to a collection of state-of-the-art approaches in the field. Our experiments further include a thorough computational analysis of the main building blocks, runtime measurements, and an analysis of storage requirements. Eventually, we identify a model configuration with low storage requirements, competitive retrieval accuracy, and a runtime behavior, which enables the deployment even on large image databases.

We study the watermarking of 2D vector data and introduce a framework which preserves topological properties of the input. Our framework is based on so-called maximum perturbation regions (MPR) of the input vertices, which is a concept similar to the just-noticeable-difference constraint. The MPRs are computed by means of the Voronoi diagram of the input and allow us to avoid (self-)intersections of input objects that might result from the embedding of the watermark. We demonstrate and analyze the applicability of this new framework by coupling it with a well-known approach to watermarking that is based on Fourier descriptors. However, our framework is general enough such that any robust scheme for the watermarking of vector data can be applied.

In this paper we present a novel watermark detector for additive spread-spectrum watermarking in the wavelet transform domain of color images. We propose to model the highly correlated DWT subbands of the RGB color channels by multivariate power-exponential distributions. This statistical model is then exploited to derive a likelihood ratio test for watermark detection. Our results indicate that joint statistical modeling of color DWT detail subbands leads to increased detection performance compared to previous approaches, namely watermarking of the luminance channel only, decorrelating the color bands, or relying on a joint Gaussian host signal model.

This letter describes an attack on the recently proposed ldquowatermarking method based on significant difference of wavelet coefficient quantizationrdquo by Lin While the method is shown to be robust against many signal processing operations, security of the watermarking scheme under intentional attack exploiting knowledge of the implementation has been neglected. We demonstrate a straightforward attack which retains the fidelity of the image. The method is therefore not suitable for copyright protection applications. Further, we propose a countermeasure which mitigates the shortcoming.

The temporal correlation between adjacent video frames poses a severe challenges for video watermarking applications. Motion-coherent watermarking has been recognized as a strategy to embed watermark information in video frames, resistant to collusion attacks. The motion-compensated temporal wavelet transform (MC-TWT) provides an efficient tool to separate static and dynamic components of a video scene and enables motion-coherent water-marking. In this paper, we extend a MC-TWT domain watermarking scheme with blind detection, i.e. motion estimation and watermark detection is performed without reference to the unwatermarked video. Our results show that motion-coherent watermarking can be combined with a blind detector, widening the applicability of MC-TWT domain watermarking beyond forensics (where the unwatermarked content is assumed to be available).

We propose to use secret, key-dependent, parametric, wavelet filters to improve the security of digital watermarking schemes operating in the wavelet transform domain. We show that the parametrization of wavelet filters can be easily integrated into existing wavelet-based watermarking algorithms, resulting in improved security without additional computational complexity. Both robustness and imperceptibility are adequate for many applications.