TY - GEN
T1 - Noisy smoothing image source identification
AU - Liu, Yuying
AU - Huang, Yonggang
AU - Zhang, Jun
AU - Liu, Xu
AU - Shen, Hualei
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Feature based image source identification plays an important role in the toolbox for forensics investigations on images. Conventional feature based identification schemes suffer from the problem of noise, that is, the training dataset contains noisy samples. To address this problem, we propose a new Noisy Smoothing Image Source Identification (NS-ISI) method. NS-ISI address the noise problem in two steps. In step 1, we employ a classifier ensemble approach for noise level evaluation for each training sample. The noise level indicates the probability of being noisy. In step 2, a noise sensitive sampling method is employed to sample training samples from original training set according to the noise level, producing a new training dataset. The experiments carried out on the Dresden image collection confirms the effectiveness of the proposed NS-ISI. When the noisy samples present, the identification accuracy of NS-ISI is significantly better than traditional methods.
AB - Feature based image source identification plays an important role in the toolbox for forensics investigations on images. Conventional feature based identification schemes suffer from the problem of noise, that is, the training dataset contains noisy samples. To address this problem, we propose a new Noisy Smoothing Image Source Identification (NS-ISI) method. NS-ISI address the noise problem in two steps. In step 1, we employ a classifier ensemble approach for noise level evaluation for each training sample. The noise level indicates the probability of being noisy. In step 2, a noise sensitive sampling method is employed to sample training samples from original training set according to the noise level, producing a new training dataset. The experiments carried out on the Dresden image collection confirms the effectiveness of the proposed NS-ISI. When the noisy samples present, the identification accuracy of NS-ISI is significantly better than traditional methods.
UR - http://www.scopus.com/inward/record.url?scp=85034255042&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69471-9_10
DO - 10.1007/978-3-319-69471-9_10
M3 - Conference contribution
AN - SCOPUS:85034255042
SN - 9783319694702
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 147
BT - Cyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
A2 - Wu, Wei
A2 - Castiglione, Aniello
A2 - Wen, Sheng
PB - Springer Verlag
T2 - 9th International Symposium on Cyberspace Safety and Security, CSS 2017
Y2 - 23 October 2017 through 25 October 2017
ER -