In an era when the protection of intellectual property rights becomes more and more important, providing robust and efficient watermarking techniques is crucial, both in terms of embedding and detection. In this paper, the authors specifically focus on the latter stage. Most often, the detection consists in the comparison of a fixed and non-adaptive decision threshold to a correlation coefficient. This threshold is usually determined either theoretically or experimentally. Here, it is proposed to apply Grubbs? test, a simple statistical test for outliers, on the correlation data in order to take a binary decision about the presence or the absence of the searched watermark. The proposed technique is applied to three algorithms from the literature: the correlation data generated by the detector is fed to Grubbs' test. The obtained results show that Grubbs' test is efficient, robust and reliable. Above all, it automatically adapts to the searched watermark and can be easily applied to most types of watermarking approaches.

More than ever, the growing amount of exchanged digital content calls for efficient and practical techniques to protect intellectual property rights. During the past two decades, watermarking techniques have been proposed to embed and detect information within these contents, with four key requirements at hand: robustness, security, capacity, and invisibility. So far, researchers mostly focused on the first three, but seldom addressed the invisibility from a perceptual perspective and instead mostly relied on objective quality metrics. In this paper, a novel DFT watermarking scheme featuring perceptually optimal visibility versus robustness is proposed. The watermark, a noise-like square patch of coefficients, is embedded by substitution within the Fourier domain; the amplitude component adjusts the watermark strength, and the phase component holds the information. A perceptual model of the human visual system (HVS) based on the contrast sensitivity function (CSF) and a local contrast pooling is used to determine the optimal strength at which the mark reaches the visibility threshold. A novel blind detection method is proposed to assess the presence of the watermark. The proposed approach exhibits high robustness to various kinds of attacks, including geometrical distortions. Experimental results show that the robustness of the proposed method is globally slightly better than state-of-the-art. A comparative study was conducted at the visibility threshold (from subjective data) and showed that the obtained performances are more stable across various kinds of content.

In this paper, we introduce a novel way to represent an image sequence, which naturally exhibits the temporal persistence of the textures. Standardized representations have been thoroughly optimized, and getting significant improvements has become more and more difficult. As an alternative, Analysis-Synthesis (AS) coders have focused on the use of texture within a video coder. We introduce here a new AS representation of image sequences that remains close to the classic block-based representation. By tracking textures throughout the sequence, we propose to reconstruct it from a set of moving textures which we call motion tubes. A new motion model is then proposed, which allows for motion field continuities and discontinuities, by hybridizing block matching and a low-computational mesh-based representation. Finally, we propose a bi-predictional framework for motion tubes management.