Non-deterministic approach to allay replay attack on iris biometric
Tóm tắt
Biometric-based verification system has emerged as a powerful authentication tool. Despite its advantages over traditional systems, it is prone to several attacks. These attacks may creep through the biometric system and may prove fatal if it is not robust enough. One such attack, known as replay attack, relates to replaying of illegally intercepted data has been least explored with respect to biometrics. The paper proposes a non-deterministic approach to iris recognition and attempts to show its utility in allaying replay attack over iris recognition system. The system determines robust iris regions for each eye using LBP-based feature extraction and involves the use of randomly selected subsets of these regions for authentication. These data, even if intercepted, are useless as the non-deterministic nature of technique will require a differently ordered subset of regions for each authentication. The performance of this system and its effectiveness in allaying replay attack has been shown experimentally. The results have been compared with existing state-of-art techniques with respect to iris recognition and replay attack. The impact of hill climbing attack on the proposed approach has also been discussed as it has been proved, by various researchers, to be critical to the performance of a biometric system.
Tài liệu tham khảo
Gupta R, Sehgal P (2016) A survey of attacks on iris biometric systems. Int J Biom 8:145–178
Daugman J (2006) Probing the uniqueness and randomness of IrisCodes: results from 200 billion iris pair comparisons. Proc IEEE 94:1927–1935
Jain AK, Kumar A (2010) Biometrics of next generation: an overview. Second Gener Biom 12:2–3. https://doi.org/10.1007/978-94-007-3892-8_3
Maltoni D, Maio D, Jain A, Prabhakar S (2009) Handbook of fingerprint recognition. Springer, Berlin
Hollingsworth KP, Bowyer KW, Flynn PJ (2009) The best bits in an iris code. IEEE Trans Pattern Anal Mach Intell 31:964–973
Rathgeb C, Uhl A, Wild P (2011) On combining selective best bits of iris codes. In: European working biometrics identity management. Springer, Berlin, Heidelberg, pp 227–237
Flom L, Safir A (1987) Iris recognition system. U.S. Patent No. 4,641,349
Daugman JG (1994) Biometric personal identification system based on iris analysis. U.S. Patent No. 5,291,560
Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85:1348–1363. https://doi.org/10.1109/5.628669
Boles WW, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46:1185–1188
He Y, Feng G, Hou Y, Li L, Micheli-Tzanakou E (2011) Iris feature extraction method based on LBP and chunked encoding. In: 2011 Seventh International Conference on Natural Computation, IEEE, pp 1663–1667
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987. https://doi.org/10.1109/TPAMI.2002.1017623
Sun Z, Tan T, Qiu X (2006) Graph matching iris image blocks with local binary pattern. In: International Conference on Biometrics. Springer, Berlin, Heidelberg, pp 366–372
Saminathan K, Chakravarthy T, Devi MC (2015) Comparative study on biometric iris recognition based on hamming distance and multi block local binary pattern. Indian J Sci Technol 8:1–8
Li C, Zhou W, Yuan S (2015) Iris recognition based on a novel variation of local binary pattern. Vis Comput 31:1419–1429
Huang D, Shan C, Ardabilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41:765–781. https://doi.org/10.1109/tsmcc.2011.2118750
Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2007) Learning multi-scale block local binary patterns for face recognition. In: International conference on biometrics. Springer, Berlin, Heidelberg, pp 828–837. https://doi.org/10.1007/978-3-540-74549-5_87
Rodriguez Y, Marcel S (2006) Face authentication using adapted local binary pattern histograms. In: European conference on computer vision. Springer, Berlin, Heidelberg, pp 321–332. https://doi.org/10.1007/11744085_25
Wang X, Gong H, Zhang H, Li B, Zhuang Z (2006) Palmprint identification using boosting local binary pattern. In: 18th International conference on pattern recognition (ICPR’06), IEEE, IEEE, pp 503–506. https://doi.org/10.1109/icpr.2006.912
Guo Z, Zhang L, Zhang D, Mou X (2010) Hierarchical multiscale LBP for face and palmprint recognition. In: 2010 IEEE international conference on image processing. IEEE, IEEE, pp 4521–4524. https://doi.org/10.1109/icip.2010.5653119
Lu Y, Yoon S, Xie SJ, Yang JC, Wang Z, Park DS (2014) Finger vein recognition using generalized local line binary pattern. TIIS 8:1766–1784
Malathi S, Meena C (2010) An efficient method for partial fingerprint recognition based on local binary pattern. In: 2010 International conference on communication control computer technology (ICCCT), IEEE, pp 569–572. https://doi.org/10.1109/icccct.2010.5670775
Czajka A, Pacut A (2008) Replay attack prevention for iris biometrics. In: 2008 42nd Annual IEEE international Carnahan conference on security technology. IEEE, pp 247–253. https://doi.org/10.1109/ccst.2008.4751309
Hämmerle-Uhl J, Raab K, Uhl A (2011) Robust watermarking in iris recognition: application scenarios and impact on recognition performance. ACM SIGAPP Appl Comput Rev 11:6–18
Shelton J, Roy K, O’Connor B, Dozier GV (2014) Mitigating iris-based replay attacks. Int J Mach Learn Comput 4:204
Bolle RM, Pankanti S, Connell JH, Ratha NK (2004) Iris individuality: a partial iris model. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 2. IEEE, pp 927–930. https://doi.org/10.1109/icpr.2004.1334411
Dozier G, Frederiksen K, Meeks R, Savvides M, Bryant K, Hopes D, Munemoto T (2009) Minimizing the number of bits needed for iris recognition via bit inconsistency and GRIT. In: 2009 IEEE workshop on computational intelligence in biometrics theory, algorithms, applications, CIB, IEEE, pp 30–37. https://doi.org/10.1109/cib.2009.4925683
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29:51–59
Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recognit 42:425–436
Khan M, Mamode H (2015) Representation of dorsal hand vein pattern using local binary patterns (LBP). In: International conference on codes, cryptology, information security. Springer, pp 331–341
Sheeba SJ, Jeya SS, Veluchamy S (2013) Security system based on iris recognition. Res J Eng Sci 2:16–21
Sutra G, Dorizzi B, Garcia-Salicetti S, Othman N (2012) A biometric reference system for iris, OSIRIS version 4.1, Telecom Sud Paris, Fr. Technical report
Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40:99–121
CASIA-Iris version 3 interval database. http://biometrics.idealtest.org/dbDetailForUser.do?id=3
Rathgeb C, Uhl A (2010) Attacking iris recognition: an efficient hill-climbing technique. In: 2010 20th International conference on pattern recognition (ICPR). IEEE, pp 1217–1220
Martinez-Diaz M, Fierrez-Aguilar J, Alonso-Fernandez F, Ortega-García J, Siguenza J (2006) Hill climbing and brute force attacks on biometric systems: a case study in match-on-card fingerprint verification. In: Carnahan conference on security technology, proceedings 2006 40th annual IEEE international, pp 151–159
Maiorana E, Hine GE, Campisi P (2015) Hill-climbing attacks on multibiometrics recognition systems. IEEE Trans Inf Forensics Secur 10:900–915. https://doi.org/10.1109/TIFS.2014.2384735
Pagnin E, Dimitrakakis C, Abidin A, Mitrokotsa A (2014) On the leakage of information in biometric authentication. In: International conference on cryptology in India. Springer, pp 265–280. https://doi.org/10.1007/978-3-319-13039-2_16
Galbally J, Fierrez J, Ortega-Garcia J, McCool C, Marcel S (2009) Hill-climbing attack to an eigenface-based face verification system. In: 2009 First IEEE international conference on biometrics, identity security, IEEE, pp 1–6. https://doi.org/10.1109/bids.2009.5507530
Gomez-Barrero M, Galbally J, Fierrez J (2014) Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recognit Lett 36:243–253. https://doi.org/10.1016/j.patrec.2013.04.029