Abstract
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1777-1781 |
| Number of pages | 5 |
| Volume | 2015-August |
| ISBN (Electronic) | 9781467369978 |
| DOIs | |
| Publication status | Published - 04 Aug 2015 |
| Event | 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brisbane Convention & Exhibition Centre, Brisbane, Australia Duration: 19 Apr 2015 → 24 Apr 2015 |
Conference
| Conference | 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
|---|---|
| Country | Australia |
| City | Brisbane |
| Period | 19/04/15 → 24/04/15 |
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A compact representation of sensor fingerprint for camera identification and fingerprint matching. / Li, Ruizhe; Li, Chang Tsun; Guan, Yu.
2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings. Vol. 2015-August IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 1777-1781 7178276.Research output: Book chapter/Published conference paper › Conference paper
TY - GEN
T1 - A compact representation of sensor fingerprint for camera identification and fingerprint matching
AU - Li, Ruizhe
AU - Li, Chang Tsun
AU - Guan, Yu
PY - 2015/8/4
Y1 - 2015/8/4
N2 - Sensor Pattern Noise (SPN) has been proved as an effective fingerprint of imaging devices to link pictures to the cameras that acquired them. In practice, forensic investigators usually extract this camera fingerprint from large image block to improve the matching accuracy because large image blocks tend to contain more SPN information. As a result, camera fingerprints usually have a very high dimensionality. However, the high dimensionality of fingerprint will incur a costly computation in the matching phase, thus hindering many interesting applications which require an efficient real-time camera matching. To solve this problem, an effective feature extraction method based on PCA and LDA is proposed in this work to compress the dimensionality of camera fingerprint. Our experimental results show that the proposed feature extraction algorithm could greatly reduce the size of fingerprint and enhance the performance in term of Receiver Operating Characteristic (ROC) curve of several existing methods.
AB - Sensor Pattern Noise (SPN) has been proved as an effective fingerprint of imaging devices to link pictures to the cameras that acquired them. In practice, forensic investigators usually extract this camera fingerprint from large image block to improve the matching accuracy because large image blocks tend to contain more SPN information. As a result, camera fingerprints usually have a very high dimensionality. However, the high dimensionality of fingerprint will incur a costly computation in the matching phase, thus hindering many interesting applications which require an efficient real-time camera matching. To solve this problem, an effective feature extraction method based on PCA and LDA is proposed in this work to compress the dimensionality of camera fingerprint. Our experimental results show that the proposed feature extraction algorithm could greatly reduce the size of fingerprint and enhance the performance in term of Receiver Operating Characteristic (ROC) curve of several existing methods.
KW - Digital forensics
KW - PCA denoising
KW - Photo-response nonuniformity noise
KW - Sensor pattern noise
UR - http://www.scopus.com/inward/record.url?scp=84946034028&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946034028&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178276
DO - 10.1109/ICASSP.2015.7178276
M3 - Conference paper
AN - SCOPUS:84946034028
VL - 2015-August
SP - 1777
EP - 1781
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -