Secure identification based on fuzzy commitment scheme for JPEG XR images
Abstract
A secure identification scheme for JPEG XR images is proposed in this paper. The aim is to securely identify JPEG XR images which are generated from the same original image under various compression levels. A property of the positive and negative signs of lapped biorthogonal transform coefficients is employed to achieve a robust scheme against JPEG XR compression. The proposed scheme is robust against a difference in compression levels, and does not produce false negative matches in any compression level. Existing conventional schemes having this property are not secure. To construct a secure identification system, we propose a novel identification system that consists of a new error correction technique and a fuzzy commitment scheme, which is a well-known biometric cryptosystem. The experimental results show the proposed scheme is effective for JPEG XR compressed video sequences in terms of the querying such as false negative and true positive matches, while keeping a high level of the security.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The system adopts fuzzy commitment-based storage of the off-chain query digest to improve security when querying and ensure tamperability of image fingerprints.
“It adopts the fuzzy commitment-based storage of the off-chain query digest to improve the security when querying, and to ensure the tamperability of the image fingerprints [46].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [46]
Cites
Juels, A., & WATTENBERG, M. P. (1999) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/319709.319714 · Cited by 1,605 · see citing works
A survey on biometric cryptosystems and cancelable biometrics
Rathgeb, C., & Uhl, A. (2011) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2011-3 · Cited by 643 · see citing works
A robust image authentication method distinguishing JPEG compression from malicious manipulation
Lin, C., & Chang, S.-F. (2001) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/76.905982 · Cited by 556 · see citing works
Robust and secure image hashing
Swaminathan, A., Mao, Y., & Wu, M. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2006.873601 · Cited by 552 · see citing works
Identification of bitmap compression history: jpeg detection and quantizer estimation
Fan, Z., & de Queiroz, R. L. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.807361 · Cited by 364 · see citing works
Lagendijk, R. L., & Barni, M. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2219653 · Cited by 334 · see citing works
The Trace transform and its applications
Kadyrov, A., & Petrou, M. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.946986 · Cited by 241 · see citing works
The JPEG XR image coding standard [Standards in a Nutshell]
Dufaux, F., Sullivan, G. J., & Ebrahimi, T. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2009.934187 · Cited by 181 · see citing works
Survey on securing data storage in the cloud
Huang, C.-T., Huang, L., Qin, Z., Yuan, H., Zhou, L. F., Varadharajan, V., & Kuo, C. ‐. J. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2014.6 · Cited by 88 · see citing works
Block Mean Value Based Image Perceptual Hashing
Yang, B., Gu, F., & Niu, X. (2006) · doi:10.1109/iih-msp.2006.265125 · Cited by 81 · see citing works
Ouyang, J., Coatrieux, G., & Shu, H. (2015) · Digital Signal Processing · doi:10.1016/j.dsp.2015.03.006 · Cited by 77 · see citing works
Block Mean Value Based Image Perceptual Hashing
Yang, B., Gu, F., & Niu, X. (2006) · Information Hiding · doi:10.1109/iih-msp.2006.66 · Cited by 57 · see citing works
One-Time Key Based Phase Scrambling for Phase-Only Correlation between Visually Protected Images
Ito, I., & Kiya, H. (2009) · EURASIP Journal on Information Security · doi:10.1155/2009/841045 · Cited by 41 · see citing works
Self-citationAn overview and evaluation of JPEG compressed domain retrieval techniques
Edmundson, D., & Schaefer, G. (2012) · Proceedings ELMAR-2012 · Cited by 33 · see citing works
One-Time Key Based Phase Scrambling for Phase-Only Correlation between Visually Protected Images
Ito, I., & Kiya, H. (2009) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2009-841045 · Cited by 23 · see citing works
Self-citationIdentification of JPEG 2000 images in encrypted domain for digital cinema
Watanabe, O., Iida, T., Fukuhara, T., et al. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414285 · Cited by 14 · see citing works
Fast Method for Joint Retrieval and Identification of JPEG Coded Images Based on DCT Sign
Arnia, F., Iizuka, I., Fujiyoshi, M., et al. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379134 · Cited by 13 · see citing works
A Robust Identification Scheme for JPEG XR Images with Various Compression Ratios
Kobayashi, H., Imaizumi, S., & Kiya, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29451-3_4 · Cited by 12 · see citing works
Self-citationHash-based identification of JPEG 2000 images in encrypted domain
Dobashi, T., Watanabe, O., Fukuhara, T., et al. (2012) · International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) · doi:10.1109/ispacs.2012.6473535 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Xu, J., Zhang, J., & Wang, J. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20020076 · Cited by 9 · see citing works
“It adopts the fuzzy commitment-based storage of the off-chain query digest to improve the security when querying, and to ensure the tamperability of the image fingerprints [46].”
- Authors
- Iida, K., Kobayashi, H., & Kiya, H.
- Affiliations
- Tokyo Metropolitan University (JP)
- Tokyo Metropolitan College of Industrial Technology (JP)
- Venue
- 2016 24th European Signal Processing Conference (EUSIPCO)
- Publisher
- IEEE
- Published
- 2016-08
- Pages
- 968-972
- OpenAlex
- W2567124838
- Cited by
- 9
- References
- 19
- Citations by year
- 2025: 1 · 2018: 3 · 2017: 4 · 2016: 1
- FWCI
- 0.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- European Signal Processing Conference (EUSIPCO) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- fuzzy commitment scheme, JPEG XR, error correction, biometric cryptosystem, compressed video
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
