640 CHAPTER 22 Image Watermarking: Techniques and Applications protection [12]. The image authentication algorithm generates a watermark according to the owner’s private key. Subsequently, the watermark is imperceptibly embedded in the image. In the authentication detection procedure, the watermar k is extracted from the image and a measure of tampering is produced for the entire image. The algorithm detects the regions of the image that are altered/unaltered and, thus, are considered nonau- thentic/authentic, respectively. The alterations that are produced by a relatively mild compression and do not change significantly the quality of the image are also detected. An example of an image authentication procedure using the image “Opera of Lyon” (http://www.petitcolas.net/fabien/watermarking/image_database/index.html), which has been used as a reference image for watermark benchmarking, is depicted in Fig. 22.8. Themethodin[12] has been extended to support tampering detection using a hierarchical structure in the detection phase that ensures accurate tamper localiza- tion [131]. A novel framework for lossless (invertible) authentication watermarking, which enables zero-distortion reconstruction of the original image upon verification, has been proposed in [132]. The framework allows authentication of the watermarked images before recovery of the original image. This reduces computational requirements in situ- ations where either the verification step fails or the zero-distortion reconstruction is not needed. The framework also enables public-key authentication without granting access to the original and allows for efficient tamper localization. Effectiveness of the framework is demonstrated by implementing it using hierarchical image authentication along with lossless generalized-least significant bit data embedding. A blind image watermarking method based on a multistage vector quantizer struc- ture, which can be used simultaneously for both image authentication and copy right protection, has been proposed in [133]. In this method, the semifragile watermark and the robust watermar k are embedded in different vector quantization stages usingdifferent techniques. Simulation results demonstrated the effectiveness of the proposed algorithm in terms of robustness and fragility. Another semifragile watermarking method that is (a) (b) (c) FIGURE 22.8 (a) Original watermarked image; (b) tampered watermarked image; (c) tampered regions. References 641 robust against lossy compression has been proposed in [134]. The proposed method uses random bias and nonuniform quantization to improve the performance of the methods proposed in [121]. Differentiating between malicious and incidental manipulations in content authen- tication remains an open issue. 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Watson Research Center, New York 23.1 INTRODUCTION The problem of resolving the identity of a person can be categor ized into two fundamen- tally distinct types of problems with different inherent complexities [1]: (i) verification and (ii) recognition. Verification (authentication) refers to the problem of confirming or denying a person’s claimed identity (Am I who I claim I am?). Recognition (Who am I?) refers to the problem of establishing a subject’s identity. 1 A reliable personal iden- tification is critical in many daily transactions. For example, access control to physical facilities and computer priv ileges are becoming increasingly important to prevent their abuse. There is an increasing interest in inexpensive and reliable personal identification in many emerging civilian, commercial, and financial applications. Typically, a person could be identified based on (i) a person’s possession (“something that you possess”), e.g., permit physical access to a building to all persons whose identity could be authenticated by possession of a key; (ii) a person’s knowledge of a piece of infor- mation (“something that you know”), e.g., permit login access to a system to a person who knows the user id and a password associated with it. Another approach to identifi- cation is based on identifying physical characteristics of the person. The characteristics could be either a person’s anatomical traits, e.g., fingerprints and hand geometry, or his behavioral characteristics, e.g., voice and signature. This method of identification of a person based on his anatomical/behavioral characteristics is called biometrics. Since these physical characteristics cannot be forgotten (like passwords) and cannot be easily shared or misplaced (like keys), they are generally considered to be a more reliable approach to solving the personal identification problem. 23.2 EMERGING APPLICATIONS Accurate identification of a person could deter crime and fraud, streamline business processes, and save critical resources. Here are a few mind boggling numbers: about one 1 Often, recognition is also referred to as identification. 649 [...]... detected If a fingerprint image is of poor quality, it is enhanced to improve the clarity of ridge/valley structures and mask out all the regions that cannot be reliably recovered The enhanced fingerprint image is fed to the minutiae extractor again The task of the authentication module is to authenticate the identity of the person who intends to access the system The person to be authenticated indicates... contact with the platen (see Fig 23.4) The rest of the imaging system essentially consists of an assembly of an LED light source and a CCD placed on the other side of the glass platen The light source illuminates the glass at a certain angle, and the camera is placed such that it can capture the light reflected from the glass The light that incidents on the platen at the glass surface touched by the ridges... fingerprint image After the orientation field is obtained, the fingerprint image can then be adaptively enhanced by using the local orientation information Let fi (x, y) (i ϭ 0, 1, 2, 3, 4, 5, 6, 7) denote the gray level value at pixel (x, y) of the filtered image corresponding to the orientation ␪i , ␪i ϭ i ∗ 22.5◦ The gray level value at pixel (x, y) of the enhanced image can be interpolated according to the. .. module is to enroll persons and their fingerprints into the system database When the fingerprint images and the user name of a person to be enrolled are fed to the enrollment module, a minutiae extraction algorithm is first applied to the fingerprint images and the minutiae patterns are extracted A quality checking algorithm is used to ensure that the records in the system database only consist of fingerprints... of the line segment joining the core and delta, and (iii) ␥, the number of ridges crossing the line segment joining core and delta The relative position, R, of the delta with respect to the symmetry axis is determined as follows: R ϭ 1 if the delta is on the right side of the symmetry axis; R ϭ 0, otherwise 3 Ridge structure: The classifier not only uses the orientation information but also utilizes the. .. coarse-level ridge map for the recoverable region The information integration is based on the observation that genuine ridges in a region evoke a strong response in the feature images extracted from the filters oriented in the direction parallel to the ridge direction in that region and at most a weak response in feature images extracted from the filters oriented in the direction orthogonal to the ridge direction... addition, observe that the distortion is nonlinear: given the amount of distortions at two arbitrary locations on the finger, it is not possible to predict the distortions at all the intervening points on the line joining the two points The adaptive elastic string matching algorithm [22] summarized in this chapter uses three attributes of the aligned minutiae for matching: its distance from the reference minutiae... denote the coordinates of the i th point on the arc length parameterized closed curve ⌿ 2 Symmetry: The feature extraction stage also estimates an axis locally symmetric to the ridge structures at the core (see Fig 23.14) and computes (i) ␣, the angle between the symmetry axis and the line segment joining core and delta, (ii) ␤, the average angle difference between the ridge orientation and the orientation... places his finger on the fingerprint scanner; a digital image of the fingerprint is captured; minutiae pattern is extracted from the captured fingerprint image and fed to a matching algorithm which matches it against the person’s minutiae templates stored in the system database to establish the identity 23.6 FINGERPRINT SENSING There are two primary methods of capturing a fingerprint image: inked (offline)... Segmentation: It is important to localize the portions of the fingerprint image depicting the finger (foreground) The simplest approach segments the foreground by global or adaptive thresholding A novel and reliable approach to segmentation by Ratha et al [35] exploits the fact that there is significant difference in the magnitudes of variance in the gray levels along and across the flow of a fingerprint ridge . image is fed to the minutiae extractor again. The task of the authentication module is to authenticate the identity of the person who intends to access the system. The person to be authenticated. embedded in the image. In the authentication detection procedure, the watermar k is extracted from the image and a measure of tampering is produced for the entire image. The algorithm detects the regions. database. When the fingerprint images and the user name of a person to be enrolled are fed to the enrollment module, a minutiae extr action algorithm is first applied to the fingerprint images and the minutiae