A watermark-embedding procedure is imperceptible if humans cannot distinguish the original data from the data with the inserted watermark.thentic. If a watermark is used for another application, however, it is desirable that the watermark always remains in the host data, even if the quality of the host data is degraded, intentionally or unintentionally. Examples of unintentional degradations are applications involving storage or transmission of data, where lossy compression techniques are applied to the data to reduce bit rates and increase efficiency. Other unintentional quality-degrading processing techniques include filtering, re-sampling, digital-analog (D/A) and analog-digital (A/D) conversion. On the other hand, a watermark can also be subjected to processing solely intended to remove the watermark [23]. In addition, when many copies of the same content exist with different watermarks, as would be the case for fingerprinting, watermark removal is possible because of collusion between several owners of copies. In general, there should be no way in which the watermark can be removed or altered without sufficient degradation of the perceptual quality of the host data so as to render it unusable. v Security: The security of watermarking techniques can be interpreted in the same way as the security of encryption techniques. Kerckhoff's assumption states that one should assume that the method used to encrypt the data is known to an unauthorized party and that the security must lie in the choice of a key [69]. Hence a watermarking technique is truly secure if knowing the exact algorithms for embedding and extracting the watermark does not help an unauthorized party to detect the presence of the watermark or remove it [97].v Oblivious versus Nonoblivious Watermarking: In some applications, like copyright protection and data monitoring, watermark extraction algorithms can use the original unwatermarked data to find the watermark. This is called nonoblivious watermarking [59]. In most other applications, e.g., copy protection and indexing, the watermark-extraction algorithms do not have access to the original unwatermarked data. This renders the watermark extraction more difficult. Watermarking algorithms of this kind are referred to as public, blind, or oblivious watermarking algorithms.The requirements listed above are all related to each other. For instance, a very robust watermark can be obtained by making many large modifications to the host data for each bit of the watermark. Large modifications in the host data will be noticeable, however, and many modifications per watermark bit will limit the maximum amount of watermark bits that can be stored in a data object. Hence, a tradeoff should be considered between the different requirements so that an optimal watermark for each application can be developed. The mutual dependencies between the basic requirements are shown in Fig. 1.The relation between the basic requirements for a well-designed secure watermark is represented in Fig. 2. The perceptual impact axis r...
Abstract-This paper proposes the differential energy watermarking (DEW) algorithm for JPEG/MPEG streams. The DEW algorithm embeds label bits by selectively discarding high frequency discrete cosine transform (DCT) coefficients in certain image regions. The performance of the proposed watermarking algorithm is evaluated by the robustness of the watermark, the size of the watermark, and the visual degradation the watermark introduces. These performance factors are controlled by three parameters, namely the maximal coarseness of the quantizer used in pre-encoding, the number of DCT blocks used to embed a single watermark bit, and the lowest DCT coefficient that we permit to be discarded. In this paper, we follow a rigorous approach to optimizing the performance and choosing the correct parameter settings by developing a statistical model for the watermarking algorithm. Using this model, we can derive the probability that a label bit cannot be embedded. The resulting model can be used, for instance, for maximizing the robustness against re-encoding and for selecting adequate error correcting codes for the label bit string.
Streaming multimedia content in real-time over a wireless link is a challenging task because of the rapid fluctuations in link conditions that can occur due to movement, interference, and so on. The popular IEEE 802.11 standard includes low-level tuning parameters like the transmission rate. Standard device drivers for today's wireless products are based on gathering statistics, and consequently, adapt rather slowly to changes in conditions. To meet the strict latency requirements of streaming applications, we designed and implemented an advanced control algorithm that uses signal-strength (SNR) information to achieve fast responses. Since SNR readings are quite noisy we do not use that information to directly control the rate setting, but rather as a safeguard limiting the range of feasible settings to choose from. We report on real-time experiments involving two laptops equipped with IEEE 802.11a wireless interface cards. The results show that using SNR information greatly enhances responsiveness in comparison to statistics-based rate controllers.
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")
The processing and encryption of multimedia content are generally considered sequential and independent operations. In certain multimedia content processing scenarios, it is, however, desirable to carry out processing directly on encrypted signals. The field of secure signal processing poses significant challenges for both signal processing and cryptography research; only few ready-to-go fully integrated solutions are available. This study first concisely summarizes cryptographic primitives used in existing solutions to processing of encrypted signals, and discusses implications of the security requirements on these solutions. The study then continues to describe two domains in which secure signal processing has been taken up as a challenge, namely, analysis and retrieval of multimedia content, as well as multimedia content protection. In each domain, state-of-the-art algorithms are described. Finally, the study discusses the challenges and open issues in the field of secure signal processing.
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