Abstract—This paper presents a preliminary study on
biometric watermarking by using offline handwritten signature. Specifically, offline handwritten signature is discritized into binary bit string as hidden biometric watermark. It is reconstructed subsequently with the intention to authenticate the claimed source of the digital document. In this paper, we focus on the study in examining the strength and robustness of three selected biometric watermarking schemes, namely Least Significant Bit (LSB) substitution, CDMA spread spectrum in spatial domain and CDMA spread spectrum in Discrete Wavelet Transform (DWT), against JPG compression. We check the distortion rate for the original and the extracted biometric bit string after JPG compression. The performance of the selected biometric watermarking schemes is validated based on human visual inspection, Peak Signal to Noise Ratio (PSNR) and distortion rate (normalized Hamming distance). Experiment results show that CDMA spread spectrum in DWT is the most promising scheme in biometric watermarking.
Index Terms—Biometric Watermarking, Offline Handwritten
Signature, Digital Documents, Authentication.
I. INTRODUCTION OWADAYS, digital media such as audio, image, video or text documents have been rapidly replacing traditional
paper-based documents in official and legal affairs. Unfortunately, new threats arisen in such a way that these documents could be illicitly accessed and altered by intruders. As a result, unlimited numbers of identical but imitative copies could be illegally duplicated with relatively ease and no loss of fidelity. Thus, authentication of the original source of the digital documents is becoming a crucial concern.
One of the well-known methods used in authentication of digital documents is cryptography. However, it is not a good candidate since the critical contents might be fully exposed to the public when it is being decrypted at the receiver end. Alternatively, digital documents can be furnished with
Manuscript received January 31, 2007. C. Y. Low is with Faculty of Information Science and Technology,
Multimedia University, Malaysia (e-mail: [email protected]). Andrew B. J. Teoh was with Faculty of Information Science and
Technology, Multimedia University, Malaysia. He is now with Biometrics Engineering Research Center (BERC), Yonsei University, South Korea (e-mail: [email protected]).
Connie Tee is with Faculty of Information Science and Technology, Multimedia University, Malaysia (e-mail: [email protected]).
information-hiding techniques [1], where steganography and digital watermarking [2] are the foremost sub-disciplines of it. Both techniques allow covert communication between two trusted parties with the assumption that the existence of the communication is transparent. As opposed to steganography, digital watermarking is well-fitted with the feature of robustness. Ideally, it should be infeasible to remove the hidden watermark contents even if the algorithm is made to be public-known.
Traditionally, watermarking schemes embed a pre-determined string such as the name of author or logo into the host document. However, this does not convincingly validate the registered originator, since anyone can watermark a particular name or logo into the host document. Biometric watermarking that uses human physiological or behavioral characteristics is increasingly receiving attention among research communities presently [5]. Biometric traits such as handwritten signature, fingerprint, iris, hand geometry, face and etc are widely employed to offer a viable constituent in the context of authentication. Biometric watermarking is initiated to promote the wide spread utilization of biometric data and furthermore securely preserve the integrity and robustness of biometric attributes due to deliberate manipulations and attacks.
Throughout this paper, we make use of offline handwritten signature of author to convey intermediate ownership as a valid originator of digital document. Offline handwritten signature is being discritized into binary bit string as biometric watermark with the intention that it is hidden in the host and perceptually undetectable from human observation. At the same time, the perceivable image quality of the underlying host should remains unaffected. The hidden biometric watermark pattern is subsequently extracted and used to verify the claimed author of the digital document.
The objective of this paper focuses on the study in examining the strengths and robustness of various biometric watermarking schemes namely Least Significant Bit (LSB) substitution, CDMA spread spectrum in spatial domain and CDMA spread spectrum in Discrete Wavelet Transform (DWT) against JPG compression. Specifically, we interested to measure the distortion rate for the original and the extracted biometric bit string after JPG compression. The performance of the selected biometric watermarking schemes is validated based on human visual inspection, Peak Signal to Noise Ratio (PSNR) and distortion rate (normalized Hamming distance).
A Preliminary Study on Biometric Watermarking for Offline Handwritten Signature
C. Y. Low, Andrew B. J. Teoh, Member, IEEE, Connie Tee
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Proceedings of the 2007 IEEE International Conference on Telecommunications andMalaysia International Conference on Communications, 14-17 May 2007, Penang, Malaysia
II. LITERATURE REVIEW F. Petitcolas, et al. [1] provides a very comprehensive survey
in digital watermarking techniques. In accordance with F. Hartung, et al. [2] and T. H. Chen, et al. [4], an ideal watermarking scheme should possess the following essential properties: 1) Invisibility
The watermark should be hidden in such a way that the watermarked image must be perceptibly identical to the original one. That is, human observers should not perceive the existence of the watermark in any circumstances. In addition, the unauthorized parties should not be able to conjecture or derive the watermark pattern by any of the statistical methods.
2) Robustness Digital documents might be subjected to intentional or unintentional attacks, such as lossy compression, filtering, resizing, rotation, cropping and etc. Therefore, an effective watermarking algorithm should be least affected in such a way that the hidden watermark must be still present under any of these manipulations.
3) Security In accordance with Kerckhoff’s principle, a cryptosystem should be still secure if the underlying algorithm, except the secret key, is public-known.
Biometric digital watermarking was primarily proposed by A. K. Jain and his biometric research team. A. K. Jain, et al. [3] proposed to use biometric data to secure another type of biometric data to increase the overall security of the system by using amplitude modulation-based watermarking method. A. M. Namboodiri and A. K. Jain [5] suggested a fragile watermarking algorithm, where a digital document is watermarked with online handwritten signature of the author by using LSB substitution. However, the proposed algorithm is easily defeated under JPG compression. M. Vatsa, et. al. [8] made use of multi-resolution DWT to embed face image in a fingerprint image. v-Support Vector Machine (SVM) is exploited to enhance the quality of the extracted face image by at least 10% of boost in recognition rate even in the presence of attacks. M. Vatsa and R. Singh [9] synergistically combined DWT and LSB biometric watermarking algorithm that securely embeds a face template in a fingerprint image. The novel algorithm demonstrated to mitigate the vulnerabilities due to both geometric and frequency attack.
III. PROPOSED BIOMETRIC FEATURE PROCESSING In order to embed the offline handwritten signature into the
host, the discritization of offline handwritten signature into biometric bit string has to be done. Initially, the offline handwritten signature is preprocessed into 300 x 200 binary images. Binarization of images may clearly isolate a single stroke or several separated strokes of handwritten signature from background.
Digitized images may contain unwanted speckle noises, smears, scratches or etc. Median filtering is then applied to doff away the existing noises to serve a flawless pattern for farther manipulations. Then, the preprocessed images are projected into feature space via Discrete Radon Transform (DRT). However, DRT produces huge feature space of high dimensionality. Principle Components Analysis (PCA) is utilized to compress the DRT features without lost of significant characteristics [10]. Lastly, PCA feature vectors are discritized into biometric bit string by using the method that proposed by P. Tuyls, et al. [7]: 1) Step 1: Input
We possess a collection of PCA feature vectors, mjnijiF ...1,...1, }{ == , where every i writer contributes m different samples of genuine offline handwritten signatures.
2) Step 2: Statistics Info Estimation Estimate the mean feature vector, of each writer i as
follow:
niFm
m
jjii ...1,1
1, == ∑
=
µ (1)
Estimate the grand mean, denoted by Gµ , is the mean of total enrolled feature vectors:
∑=
=n
iiG n 1
1 µµ (2)
3) Step 3: Quantization Derive biometric binary bit string (Qi)t as theorem below:
>≤
=tti
ttiti if
ifQ
)()(1)()(0
)(µµµµ (3)
4) Step 4: Reliable Bit Extraction Compute the variance of t-th bit of each user i with respect to μi:
2
1,
2, ])()[(
11 ∑
=
−−
=m
jtitjiti F
mS µ (4)
Determine the reliability (Ri)t of t-th bit of user i:
−+=
2,2
)()(1
21)(
ti
ttiti
SerfR
µµ (5)
where erf refers to the error function. 5) Step 5: In accordance with the significance of reliability
Ri, the K most reliable biometric bit pattern Zi of each writer i is derived from Qi.
IV. BIOMETRIC WATERMARK EMBEDDING AND EXTRACTION After the offline handwritten signature is discritized into
biometric bit string format, we embed the biometric bit string into the host by using the following schemes: LSB substitution, CDMA spread spectrum in spatial domain and CDMA spread spectrum in DWT.
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A. LSB Substitution LSB is the simplest and straightforward method to spatially
integrate the biometric watermark bit string into the host by slightly readjusted the least significant bit of the randomly selected pixels. The least significant bit might be merely carries meaningless noise pattern. Thus, the biometric watermark bit string can be integrated directly into the least significant bit without any noticeable changes of the visual image quality.
B. CDMA Spread Spectrum in Spatial Domain CDMA spread spectrum in digital watermarking uses the
idea borrowed from the communication field [2]. It appears to consecutively distribute the biometric watermark bit string across the entire host image. Basically, the biometric watermark is first transformed into the sequence of PN, which is then weighted with a scaling factor of α and integrated into the host image I(x, y) as equation below: PNyxIyxIw *),(),( α+= (6) where Iw(x, y) denotes the watermarked image. The robustness level of the hidden biometric watermark could be optimized by enlarging the value of gain factor α with the expense of the visual quality of the watermarked image.
C. CDMA Spread Spectrum in DWT Host image can be further transformed into its equivalent
frequency domains. DWT decomposes the host image into approximation frequency coefficients (LL) and details frequency coefficients (HL, LH, and HH, which are known as horizontal, vertical and diagonal component respectively). The process of the decomposition may be repeated into multiple scales to earn extra preciseness at the endpoint of watermark detection while sacrifice possible quality demotion of the watermarked image. The concept of CDMA spread spectrum can be exploited into DWT mid-frequency bands (HL and LH only). PN sequence is generated and a single bit of biometric watermark bit string is encoded into both HL and LH as equation below:
∈∈⋅+
=HHLLvuvuI
LHHLvuPNvuIvuIw ,,,),(
,,,),(),(
α (7)
where I(u, v) denotes DWT mid-frequency coefficients and Iw(u, v) refers to the watermarked image. It is noted that DWT coefficients in LL and HH should remain unaffected due to the certainties that manipulation of LL tends to degrade the image quality while FF is breakable against lossy compression. The scaling factor α is utilized to enhance the accuracy rate of biometric watermark recovery. Larger α may lead to additional image degradation.
V. EVALUATION SCHEMES An independent offline handwritten signature database is
used throughout the experiments. It is comprised of 500 sets of genuine offline handwritten signatures from 50 writers. Due to the non-repetitive variation of handwritten signature, the template enrollment was completed within two different contact sessions. At the first contact, each of the writers was requested
to register 5 samples of handwritten signature, which yielding a subset of 250 samples. During the second contact (two weeks after the prior contact), each of the same writers was again called to provide another 5 samples of handwritten signature. Thereby, a database that contains 500 templates of handwritten signature was yielded.
A series of experiments were conducted to demonstrate the feasibility of the selected biometric watermarking schemes: LSB, CDMA spread spectrum in spatial domain and CDMA spread spectrum in DWT. Fig. 1 shows the image of Fish Leong, which to be employed as the host image throughout the experiments. It is coded in gray level bitmap (BMP) with the size of 335 x 392 pixels.
Fig. 1. Host Image
As discussed beforehand, the offline handwritten signature
has to be discritized into biometric bit string of three different bit lengths, namely 10, 50, and 100 bits. We examine the strength and robustness of the selected biometric watermarking schemes in two aspects mentioned below: 1) The visual quality of watermarked image must be as close
as the original host such that human observer should not be able to perceive the existence of the hidden biometric watermark in any circumstances. Furthermore, PSNR is employed to measure the image quality.
2) The distortion rate of the original and the extracted biometric bit string after JPG compression is measured with respect to three different JPG quality ratios of 20, 50, and 80. Higher JPG quality ratio may produce higher quality of images with less sensible degradation due to lossy compression.
A. Peak Signal to Noise Ratio (PSNR) The PSNR of each watermarked image is estimated due the
fact that PSNR is the most common scheme of image quality measurement [6]. However, PSNR only provides a rough estimation of the watermarked image irrespective of its actual visual quality. Since the PSNR does not take the aspect of visual inspection into consideration, image with high PSNR might be not necessary looks better. PSNR is defined via the Mean Square Error (MSE) in logarithmic decibel (dB) as:
21
0
1
0)],(),([1 ∑∑
−
=
−
=
−=m
i
n
jw jiIjiI
mnMSE (8)
)(log102
10 MSEMAXPSNR I⋅= (9)
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where I refers to the original host image of size m x n, and wI implies watermarked image. IMAX indicates the maximum pixel value of the original host image. General speaking, the visual quality of the watermarked image should be still acceptable to the human visual inspection for any PSNR value greater than 30dB [6].
B. Distortion Rate of Biometric Bit String The distortion rate for the original and the extracted
biometric bit string after JPG compression is measured as follow:
KHRateDistortion = (10)
where H and K denote Hamming distance and biometric bit length respectively. The experimental results will be discussed in the following section.
VI. EXPERIMENTAL RESULTS
A. LSB Substitution Fig. 2(a), (b) and (c) depict the LSB watermarked images
with biometric bit length of 10, 50, and 100 bits respectively. Not surprisingly, LSB watermarked images show unnoticeable degradation in term of the visual image quality. Table I summarizes that the values of PSNR are still above 80 dB for all cases. Moreover, the embedded biometric bit string could be recovered perfectly with zero detection error before the watermarked images are subjected to JPG compression. However, the JPG compression defeats the hidden biometric bit string, leaving with at least 0.42 of distortion rate even at the JPG quality ratio of 80. Thus, LSB is proven to be the simplest and straightforward method in biometric watermarking but it is highly fragile against JPG compression.
B. CDMA Spread Spectrum in Spatial Domain CDMA spread spectrum in spatial domain is demonstrated to
be robust against high level of JPG compression with only 0.20 of detection error at the biometric bit length of 10 and JPG quality ratio of 20 (see Table I). However, it suffers from the limitation of biometric bit length. From experiments, the watermarked image shows little to no degradation at the biometric bit length of 10 bits (see Fig. 3(a)), while the perceived image quality drops off at the longer biometric bit length of 50 and 100 bits (see Fig. 3(a) and (b)).
C. CDMA Spread Spectrum in DWT Fig. 4(a), (b) and (c) depict the watermarked images of
different biometric bit lengths by applying CDMA spread spectrum in DWT coefficient domains. This algorithm is proven to be able to preserve the image quality from severe degradation even at the longest biometric bit length of 100. Additionally, with a minimal value of gain factor 2=α , it appears to be highly resistant against JPG compression with only 0.02 and 0.08 of distortion rate under the JPG quality ratio of 20 (see Table I).
VII. CONCLUSION The core of this paper focuses on the study in checking the
strengths and robustness of prior selected biometric watermarking schemes: LSB, CDMA spread spectrum in spatial domain and CDMA spread spectrum in DWT against JPG compression. The performance of the selected biometric watermarking schemes is validated based on human visual inspection, PSNR and the distortion rate of extracted biometric bit string. From experiment, LSB is proven to be the simplest and straightforward method in biometric watermarking but it is highly fragile against JPG compression. In contrast, we observe that CDMA spread spectrum in spatial domain gives better results in the presence of JPG compression. CDMA spread spectrum in DWT is relatively the most promising one among the selected biometric watermarking schemes due to the flawlessness of the experiment results.
The upcoming studies will be continued with the robustness testing against additive of Gaussian noise, median filtering, cropping, resizing and rotation. Moreover, at least an additional of two images with different complexity will be selected as host to further demonstrate the feasibility of biometric watermarking.
REFERENCES [1] F. Petitcolas, R. Anderson and M. Kuhn, “Information Hiding - A
Survey,” Proc. IEEE, vol. 87, no. 7, pp. 1062-1078, July 1999. [2] F. Hartung and M. Kutter, “Multimedia Watermarking,” Proc. IEEE, vol.
87, no. 7, pp. 1079-1107, July 1999. [3] A. K. Jain and U. Uludag, “Hiding Biometric Data,” Proc. of the IEEE,
vol. 25, no. 11, pp. 1494-1498, November 2003. [4] T. H. Chen, G. Horng and S. H. Wang, “A Robust Wavelet-Based
Watermarking Scheme Using Quantizatin and Human Visual System Model,” Pakistan Journal of Information and Technology, vol. 2, no. 3, pp. 213-230, 2003.
[5] A. M. Namboodiri and A. K. Jain, “Multimedia Document Authentication using On-line Signature as Watermarks,” Proc. of the International Society for Optical Engineering (SPIE), 2004.
[6] S. G.rgic, M. G.rgic and M. Mrak, “Reliability of Objective Picture Quality Measures,” Proc. of the IEEE Journal of Electrical Enginerring, vol. 55, no. 1-2, pp. 3-10, 2004.
[7] P. Tuyls, A. H. M. Akkermans and Tom A. M. Kevenaar, “Face Recognition with Renewable and Privacy Preserving Binary Templates,” 4th IEEE Workshop on Automatic Identification Advanced Technologies, 2005.
[8] M. Vatsa, R. Singh and A. Noore, “Improving Biometric Recognition Accuracy and Robustness using DWT and SVM-Watermarking,” IEICE Transactions on Fundamentals of Electronics, 2005.
[9] M. Vatsa and R. Singh, “Robust Biometric Image Watermarking for Fingerprint and Face Template Protection,” IEICE Transactions on Fundamentals of Electronics, 2006.
[10] S. Y. Ooi, Andrew Teoh Beng Jin and David Ngo Chek Ling, “Offline Signature Verification through Discrete Radon Transform and Principal Component Analysis,” Proc. of International Conference on Computer and Communication Engineering (ICCCE), 2006.
C. Y. Low received his B. IT (HONS) Data Communications from Multimedia University, Malaysia in the year of 2004. Currently, he is doing towards his master degree in Multimedia University. At the same time, he is working as
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tutor of Faculty of Information Science and Technology, Multimedia University. Andrew B. J. Teoh obtained his B. Eng (Electronic) in 1999 and PhD degree in 2003 from National University of Malaysia. He was a senior lecturer and associate dean of Faculty of Information Science and Technology, Multimedia University, Malaysia. He held the post of Chairman in Center of Excellent in Biometrics and Bioinformatics in the same university. He also serves as a research consultant for Corentix Technologies in the research of biometrics system development and deployment. He has published more than 100 international journals and conference papers. Currently, he attaches to
Biometrics Engineering Research Center (BERC) in Yonsei University, Seoul as a Research Professor. His research interest is in biometrics security, pattern recognition and image processing. Connie Tee obtained her B. IT (HONS) Information Systems Engineering in 2002 and Master of Science (IT) in 2005 from Multimedia University, Malaysia. She is currently a lecturer of Faculty of Information Science and Technology, Multimedia University. Her research interests include biometrics palmprint and gait recognitions, image processing, and computer vision.
TABLE I. Experimental Results
Algorithm Least Significant Bit (LSB) CDMA in Spatial Domain CDMA in DWT
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