Fast watermarking of MPEG-1/2 streams using compressed-domain perceptual embedding and a generalized correlator detector

A novel technique is proposed for watermarking of MPEG-1 and MPEG-2 compressed video streams. The proposed scheme is applied directly in the domain of MPEG-1 system streams and MPEG-2 program streams (multiplexed streams). Perceptual models are used during the embedding process in order to avoid degradation of the video quality. The watermark is detected without the use of the original video sequence. A modified correlation-based detector is introduced that applies nonlinear preprocessing before correlation. Experimental evaluation demonstrates that the proposed scheme is able to withstand several common attacks. The resulting watermarking system is very fast and therefore suitable for copyright protection of compressed video

Spread spectrum-based video watermarking algorithms for copyright protection

Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can now benefit from hardware and software which was considered state-of-the-art several years ago. The advantages offered by the digital technologies are major but the same digital technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly possible and relatively easy, in spite of various forms of protection, but due to the analogue environment, the subsequent copies had an inherent loss in quality. This was a natural way of limiting the multiple copying of a video material. With digital technology, this barrier disappears, being possible to make as many copies as desired, without any loss in quality whatsoever. Digital watermarking is one of the best available tools for fighting this threat. The aim of the present work was to develop a digital watermarking system compliant with the recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark can be inserted in either spatial domain or transform domain, this aspect was investigated and led to the conclusion that wavelet transform is one of the best solutions available. Since watermarking is not an easy task, especially considering the robustness under various attacks several techniques were employed in order to increase the capacity/robustness of the system: spread-spectrum and modulation techniques to cast the watermark, powerful error correction to protect the mark, human visual models to insert a robust mark and to ensure its invisibility. The combination of these methods led to a major improvement, but yet the system wasn't robust to several important geometrical attacks. In order to achieve this last milestone, the system uses two distinct watermarks: a spatial domain reference watermark and the main watermark embedded in the wavelet domain. By using this reference watermark and techniques specific to image registration, the system is able to determine the parameters of the attack and revert it. Once the attack was reverted, the main watermark is recovered. The final result is a high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen

Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking

In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection

Stealthy MTD against unsupervised learning-based blind FDI Attacks in power systems

This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability

Neyman-Pearson Decision in Traffic Analysis

The increase of encrypted traffic on the Internet may become a problem for network-security applications such as intrusion-detection systems or interfere with forensic investigations. This fact has increased the awareness for traffic analysis, i.e., inferring information from communication patterns instead of its content. Deciding correctly that a known network flow is either the same or part of an observed one can be extremely useful for several network-security applications such as intrusion detection and tracing anonymous connections. In many cases, the flows of interest are relayed through many nodes that reencrypt the flow, making traffic analysis the only possible solution. There exist two well-known techniques to solve this problem: passive traffic analysis and flow watermarking. The former is undetectable but in general has a much worse performance than watermarking, whereas the latter can be detected and modified in such a way that the watermark is destroyed. In the first part of this dissertation we design techniques where the traffic analyst (TA) is one end of an anonymous communication and wants to deanonymize the other host, under this premise that the arrival time of the TA\u27s packets/requests can be predicted with high confidence. This, together with the use of an optimal detector, based on Neyman-Pearson lemma, allow the TA deanonymize the other host with high confidence even with short flows. We start by studying the forensic problem of leaving identifiable traces on the log of a Tor\u27s hidden service, in this case the used predictor comes in the HTTP header. Afterwards, we propose two different methods for locating Tor hidden services, the first one is based on the arrival time of the request cell and the second one uses the number of cells in certain time intervals. In both of these methods, the predictor is based on the round-trip time and in some cases in the position inside its burst, hence this method does not need the TA to have access to the decrypted flow. The second part of this dissertation deals with scenarios where an accurate predictor is not feasible for the TA. This traffic analysis technique is based on correlating the inter-packet delays (IPDs) using a Neyman-Pearson detector. Our method can be used as a passive analysis or as a watermarking technique. This algorithm is first made robust against adversary models that add chaff traffic, split the flows or add random delays. Afterwards, we study this scenario from a game-theoretic point of view, analyzing two different games: the first deals with the identification of independent flows, while the second one decides whether a flow has been watermarked/fingerprinted or not

Statistical Tools for Digital Image Forensics

A digitally altered image, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic image. The tampering, however, may disturb some underlying statistical properties of the image. Under this assumption, we propose five techniques that quantify and detect statistical perturbations found in different forms of tampered images: (1) re-sampled images (e.g., scaled or rotated); (2) manipulated color filter array interpolated images; (3) double JPEG compressed images; (4) images with duplicated regions; and (5) images with inconsistent noise patterns. These techniques work in the absence of any embedded watermarks or signatures. For each technique we develop the theoretical foundation, show its effectiveness on credible forgeries, and analyze its sensitivity and robustness to simple counter-attacks