Concealment of iris features based on artificial noises |
Jiao, Wenming
(Jiangsu Ocean University)
Zhang, Heng (Jiangsu Ocean University) Zang, Qiyan (Jiangsu Ocean University) Xu, Weiwei (Jiangsu Ocean University) Zhang, Shuaiwei (Jiangsu Ocean University) Zhang, Jian (Jiangsu Ocean University) Li, Hongran (Jiangsu Ocean University) |
1 | T. Root et al, Fingerprints of global warming on wild animals and plants, Nature 421 (2003), no. 6918, 57-60. DOI |
2 | Y. Zheng, D. K. Pal, and M. Savvides, Ring loss: Convex feature normalization for face recognition, in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., Salt Lake Sity, Canada, June. 2018, pp. 5089-5097. |
3 | M. Mottalli, M. Mejail, and J. Jacobo-Berlles, Flexible image segmentation and quality assessment for real-time iris recognition, in Proc. IEEE Int. Conf. Image Process. Cairo, Eqypt, Nov. 2010, pp. 1941-1944. |
4 | J. Fridrich, Image encryption based on chaotic maps, in Proc. IEEE Int. Conf. Syst. Man, Cybernetics, Orlando, FL, USA, Oct. 1997, pp. 1105-1110. |
5 | L. E. Ali, J. Luo, and J. Ma, Iris recognition from distant images based on multiple feature descriptors and classifiers, in Proc. IEEE Int. Conf. Signal Process., Chengdu, China, Nov. 2016, pp. 1357-1362. |
6 | K. Wang and K. Ajay, Cross-spectral iris recognition using cnn and supervised discrete hashing, Pattern Recogn. 86 (2019), 85-98. DOI |
7 | Z. Zhang et al, Bilateral privacy-preserving utility maximization protocol in database-driven cognitive radio networks, IEEE Trans. Depend. Secure Comput. (preprint), https://doi.org/10.1109/TDSC.2017.2781248. |
8 | J. Fridrich, Symmetric ciphers based on two-dimensional chaotic maps, Int. J. Bifurcat. Chaos 8 (1998), no. 6, 1259-1284. DOI |
9 | K. Simoens et al, A framework for analyzing template security and privacy in biometric authentication systems, IEEE Trans. Inf. Forensics Secur. 7 (2012), no. 2, 833-841. DOI |
10 | W. A. A. Torres, N. Bhattacharjee, and B. Srinivasan, Effectiveness of fully homomorphic encryption to preserve the privacy of biometric data, in Proc. Int. Conf. Inf. Integ. Web-based Applicat. Serv., Hanoi, Viet Nam, Dec. 2014, pp. 152-158. |
11 | J. Bringer et al, Faster secure computation for biometric identification using filtering, in Proc. Int. Conf. Biometrics, New Delhi, India. 2013, pp. 257-264. |
12 | A. Martin et al, Deep learning with differential privacy, in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., Vienna, Austria, Oct. 2016, pp. 308-318. |
13 | B. Hayes, Cloud computing, Web Sci. 51 (2008), no. 7, 9-11. |
14 | J. P. Hespanha, P. Naghshtabrizi, and Y. Xu, A survey of recent results in networked control systems, Proc. IEEE 95 (2007), no. 1, 138-162. DOI |
15 | H. Zhang et al, Privacy and performance trade-off in cyber-physical systems, IEEE Netw. 30 (2016), no. 2, 62-66. DOI |
16 | N. Srichumroenrattana, R. Lipikorn, and C. Lursinsap, Stereoscopic face reconstruction from a single 2-dimensional face image using orthogonality of normal surface and y-ratio, Int. J. Pattern Recognit. Artif. Intell. 30 (2016), no. 2, 1-27. |
17 | B. Benjamin et al, DP-finder: Finding differential privacy violations by sampling and optimization, in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., Toronto, Canada, Oct. 2018, pp. 508-524. |
18 | R. Venkatesan, S.-M. Koon, and M. Jakubowski, Robust image hashing, in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2000, pp. 664-666. |
19 | C. Thee et al, MVG mechanism: differential privacy under matrixvalued query, in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., Toronto, Canada, Oct. 2018, pp. 1-17. |
20 | A. Bhowmick et al, Protection against reconstruction and its applications in private federated learning, Mach. Learn. 1812 (2018), 1-45. |
21 | T. Kalker, J. Haitsma, and J. Oostveen, Issues with digital watermarking and perceptual hashing, in Proc. Int. Symp. Converg. IT Commun., Denver, CO, USA, 2001, pp. 189-197. |
22 | J. Fridrich and M. Goljan, Robust hash functions for digital watermarking, in Proc. Int. Conf. Inf. Technol. Coding Comput., Las Vegas, NV, USA, Mar. 2000, pp. 1-6. |
23 | L. Chun-Shien et al, Robust mesh-based hashing for copy detection and tracing of images, in Proc. IEEE Int. Conf. Multimedia Expo, Taipei, Taiwan, June 2004, pp. 731-734. |
24 | S. Kozat, R. Venkatesan, and M. K. Mihcak, Robust perceptual image hashing via matrix invariants, in Proc. Int. Conf. Image Process., Singapore, Oct. 2004, pp. 3443-3446. |
25 | Z. Tang et al, Robust image hashing using ring-based entropies, Signal Process. 93 (2013), no. 7, 2061-2069. DOI |
26 | J. Soria-Comas et al, Individual differential privacy: a utilitypreserving formulation of differential privacy guarantees, IEEE Trans. Inf. Forensics Secur. 12 (2017), no. 6, 1418-1429. DOI |
27 | Z. Liu et al, Contextual hashing for large-scale image search, IEEE Trans. Image Process. 23 (2014), no. 4, 1606-1614. DOI |
28 | M. De Marsico et al, Firme: face and iris recognition for mobile engagement, Image Vis. Comput. 32 (2014), no. 12, 1161-1172. DOI |
29 | W. Boles, A security system based on human iris identification using wavelet transform, in Proc. Int. Conf. Convent. Knowl. Based Intell. Electron. Syst., Adelaide, Australia, May 1997, pp. 533-541. |
30 | C. Dwork, Differential privacy, in Proc. Int. Colloquium Automata Lang. Program., Venice, Italy, July 2006, pp. 1-12. |
31 | S. Goryczka and X. Li, A comprehensive comparison of multiparty secure additions with differential privacy, IEEE Trans. Dependable Secure Comput. 14 (2017), no. 5, 463-477. DOI |
32 | F. Liu, Generalized gaussian mechanism for differential privacy, IEEE Trans. Knowl. Data Eng. 31 (2019), no. 4, 747-756. DOI |
33 | Y. Feng et al, Visual tracking via multi-experts combined with average hash model, Proc. Asian Conf. pattern recognition. (2016), 331-335. |
34 | X. Niu and Y. Jiao, An overview of perceptual hashing, Acta Electronica Sinica 36 (2008), no. 7, 1405-1411. DOI |
35 | H. Wei, J. X. Yu, and C. Lu, String similarity search: a hashbased approach, IEEE Trans. Knowl. Data Eng. 30 (2018), no. 1, 170-184. DOI |
36 | G. Vrcek and P. Peer, Iris-based human verification system: a research prototype, in Proc. Int. Conf. Syst., Chalkida, Greece, June 2009, pp. 1-4. |