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...

One way of recovering watermarks in geometrically distorted images is by performing a geometrical search. In addition to the computational cost required for this method, this paper considers the more important problem of false positives. The maximal number of detections that can be performed in a geometrical search is bounded by the maximum false positive detection probability required by the watermark application. We show that image and key dependency in the watermark detector leads to different false positive detection probabilities for geometrical searches for different images and keys. Furthermore, the image and key dependency of the tested watermark detector increases the random-imagerandom-key false positive detection probability, compared to the Bernoulli experiment that was used as a model.

Face recognition is gaining enormous interest nowadays. However, the technical challenges to "teach" a computer to recognize faces have been very difficult. Many methods and approaches have been proposed in the literature. This paper presents a face recognition method based on the combined kernel principal component analysis (KPCA) and support vector machine (SVM) methods. First, the KPCA method is utilized to extract features from the input images. The SVM method is then applied to these extracted features to classify the input images. We compare the performance of this face recognition method to other commonly-used methods. Our experiments show that the combination of KPCA and SVM achieves a higher performance compared to the nearest neighbor classifier, support vector machine, and the combination of kernel principal component analysis and nearest neighbor classifier.

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We present in this paper the results of our study on the human perception of geometric distortions in images. The ultimate goal of this study is to devise an objective measurement scheme for geometric distortions in images, which should have a good correspondence to human perception of the distortions. The study is divided into two parts. The first part of the study is the design and implementation of a user-test to measure human perception of geometric distortions in images. The result of this test is then used as a basis to evaluate the performance of the second part of the study, namely the objective quality measurement scheme. Our experiment shows that our objective quality measurement has good correspondence to the result of the user test and performs much better than a PSNR measurement.