TY - GEN
T1 - Evaluation of Denoising Algorithms for Source Camera Linking
AU - Salazar, Diego Azael
AU - Ramirez-Rodriguez, Ana Elena
AU - Nakano, Mariko
AU - Cedillo-Hernandez, Manuel
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In the source camera linking (SCL) tasks, a large number of images taken by the same camera is not available, then forensic investigation must be carried out using only residual noises extracted from each natural image without any knowledge about their source camera. Therefore, an efficient denoising algorithm and/or noise enhancement function are required to estimate accurately Sensor Pattern Noise (SPN). In this paper we provide a systematic evaluation of common-used denoising algorithms with different parameters under a SCL task. The denoising algorithms considered are locally adaptive window-based denoising and the Block-matching 3D (BM3D) denoising. The SCL task used for evaluation is image clustering based on their source camera, in which we construct three sets with natural images taken by 5, 10 and 15 different source cameras. Linkage clustering algorithm with Ward modality is applied to group the images by their source camera. The experimental results show that the BM3D denoising with standard (σ) deviation of 5 provides the best performance. Said method achieved a clustering accuracy of 98%, 96% and 85% for 5, 10 and 15 cameras respectively.
AB - In the source camera linking (SCL) tasks, a large number of images taken by the same camera is not available, then forensic investigation must be carried out using only residual noises extracted from each natural image without any knowledge about their source camera. Therefore, an efficient denoising algorithm and/or noise enhancement function are required to estimate accurately Sensor Pattern Noise (SPN). In this paper we provide a systematic evaluation of common-used denoising algorithms with different parameters under a SCL task. The denoising algorithms considered are locally adaptive window-based denoising and the Block-matching 3D (BM3D) denoising. The SCL task used for evaluation is image clustering based on their source camera, in which we construct three sets with natural images taken by 5, 10 and 15 different source cameras. Linkage clustering algorithm with Ward modality is applied to group the images by their source camera. The experimental results show that the BM3D denoising with standard (σ) deviation of 5 provides the best performance. Said method achieved a clustering accuracy of 98%, 96% and 85% for 5, 10 and 15 cameras respectively.
KW - Denoising
KW - Hierarchical clustering
KW - Photo Response Non-Uniformity (PRNU)
KW - SPN enhancement
KW - Sensor Pattern Noise (SPN)
KW - Source camera linking
UR - http://www.scopus.com/inward/record.url?scp=85111398271&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77004-4_27
DO - 10.1007/978-3-030-77004-4_27
M3 - Contribución a la conferencia
AN - SCOPUS:85111398271
SN - 9783030770037
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 282
EP - 291
BT - Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
A2 - Roman-Rangel, Edgar
A2 - Kuri-Morales, Ángel Fernando
A2 - Martínez-Trinidad, José Francisco
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Olvera-López, José Arturo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Mexican Conference on Pattern Recognition, MCPR 2021
Y2 - 23 June 2021 through 26 June 2021
ER -