[1] Sandoval Orozco A L, Arenas González D M, García Villalba L J et al. Analysis of errors in EXIF metadata on mobile devices[J]. Multimedia Tools and Applications, 74, 4735-4763(2015). http://link.springer.com/article/10.1007/s11042-013-1837-6

[2] Tian H W, Zhao Y, Ni R R et al. LDFT-based watermarking resilient to local desynchronization attacks[J]. IEEE Transactions on Cybernetics, 43, 2190-2201(2013).

[3] Tian H W, Xiao Y H, Cao G et al. Robust watermarking of mobile video resistant against barrel distortion[J]. China Communications, 13, 131-138(2016).

[4] Tian H W, Zhao Y, Wang Z et al. Robust multi-bit watermarking for free-view television using light field rendering[J]. IEICE Transactions on Information and Systems, 96, 2820-2829(2013).

[5] Tian H W, Zhao Y, Ni R R et al. Geometrically robust image watermarking by sector-shaped partitioning of geometric-invariant regions[J]. Optics Express, 17, 21819-21836(2009).

[6] Kurosawa K, Kuroki K, Saitoh N. CCD fingerprint method-identification of a video camera from videotaped images[J]. Proceedings 1999 International Conference on Image Processing, 3, 537-540(1999).

[7] Geradts Z J, Bijhold J, Kieft M et al. Methods for identification of images acquired with digital cameras[J]. Proceedings of SPIE, 4232, 505-512(2001).

[8] Bayram S, Sencar H, Memon N et al. Source camera identification based on CFA interpolation. [C]//IEEE International Conference on Image Processing 2005, September 14, 2005, Genova, Italy. New York: IEEE, 8835799(2005).

[9] Swaminathan A, Wu M. Liu K J R. Nonintrusive component forensics of visual sensors using output images[J]. IEEE Transactions on Information Forensics and Security, 2, 91-106(2007).

[10] Sorell M J. Digital camera source identification through JPEG quantisation[M]. //Sorell M J. Multimedia forensics and security. Pennsylvania: IGI Global, 291-313(2008).

[11] Alles E J. Geradts Z J M H, Veenman C J. Source camera identification for heavily JPEG compressed low resolution still images[J]. Journal of Forensic Sciences, 54, 628-638(2009).

[12] Choi K S, Lam E Y. Wong K K Y. Source camera identification using footprints from lens aberration[J]. Proceedings of SPIE, 6069, 60690J(2006).

[13] Van L T, Emmanuel S, Kankanhalli M S. Identifying source cell phone using chromatic aberration. [C]//2007 IEEE International Conference on Multimedia and Expo, July 2-5, 2007, Beijing, China. New York: IEEE, 883-886(2007).

[14] Kharrazi M, Sencar H T, Memon N. Blind source camera identification. [C]//2004 International Conference on Image Processing, 2004, October 24-27, 2004, Singapore, Singapore. New York: IEEE, 709-712(2004).

[15] Çeliktutan O, Sankur B, Avcibaş I. Blind identification of source cell-phone model[J]. IEEE Transactions on Information Forensics and Security, 3, 553-566(2008).

[16] Lukas J, Fridrich J, Goljan M. Digital camera identification from sensor pattern noise[J]. IEEE Transactions on Information Forensics and Security, 1, 205-214(2006).

[17] Thai T H, Cogranne R, Retraint F. Camera model identification based on the heteroscedastic noise model[J]. IEEE Transactions on Image Processing, 23, 250-263(2014).

[18] Popescu A C, Farid H. Exposing digital forgeries by detecting traces of resampling[J]. IEEE Transactions on Signal Processing, 53, 758-767(2005).

[19] Popescu A C, Farid H. Exposing digital forgeries in color filter array interpolated images[J]. IEEE Transactions on Signal Processing, 53, 3948-3959(2005).

[20] Johnson M K, Farid H. Exposing digital forgeries by detecting inconsistencies in lighting. [C]//Proceedings of the 7th workshop on Multimedia and security-MM&Sec’05, August 1-2, 2005, New York, NY, USA. New York: ACM, 1-10(2005).

[21] Lin Z, Wang R, Tang X et al. Detecting doctored images using camera response normality and consistency. [C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 1087-1092(2005).

[22] Chen W, Shi Y Q, Su W. Image splicing detection using 2D phase congruency and statistical moments of characteristic function[J]. Proceedings of SPIE, 6505, 65050R(2007).

[23] Kivanc Mihcak M, Kozintsev I, Ramchandran K. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. [C]//1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings, March 15-19, 1999, Phoenix, AZ, USA. New York: IEEE, 3253-3256(1999).

[24] Kang X G, Chen J S, Lin K R et al. A context-adaptive SPN predictor for trustworthy source camera identification[J]. EURASIP Journal on Image and Video Processing, 2014, 19(2014).

[25] Cooper A J. Improved photo response non-uniformity (PRNU) based source camera identification[J]. Forensic Science International, 226, 132-141(2013).

[26] Zeng H, Kang X G. Fast source camera identification using content adaptive guided image filter[J]. Journal of Forensic Sciences, 61, 520-526(2016).

[27] Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007).

[28] Xu J, Zhang L, Zhang D. A trilateral weighted sparse coding scheme for real-world image denoising[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11212, 21-38(2018).

[29] Lü J R, Luo X G, Qi S F et al. Image denoising using weighted nuclear norm minimization with preserving local structure[J]. Laser & Optoelectronics Progress, 56, 161006(2019).

[30] Liu Y C, Zhao C H, Xu Q. Neural network-based noise suppression algorithm for star images captured during daylight hours[J]. Acta Optica Sinica, 39, 0610003(2019).

[31] Cortiana A, Conotter V, Boato G et al. Performance comparison of denoising filters for source camera identification[J]. Proceedings of SPIE, 7880, 788007(2011).

[32] Chierchia G, Parrilli S, Poggi G et al. On the influence of denoising in PRNU based forgery detection. [C]//MiFor’10: Proceedings of the 2nd ACM Workshop on Multimedia in Forensics, Security and Intelligence, October 29, 2010, Firenze, Italy. New York: ACM, 117-122(2010).

[33] Zhang Y S, Tian H W, Xiao Y H et al. PRNU extraction algorithm based on trilateral weighted sparse coding model[J]. Journal of Applied Sciences, 37, 704-710(2019).

[34] Chen M, Fridrich J, Goljan M et al. Determining image origin and integrity using sensor noise[J]. IEEE Transactions on Information Forensics and Security, 3, 74-90(2008).

[35] Lawgaly A, Khelifi F, Bouridane A. Weighted averaging-based sensor pattern noise estimation for source camera identification. [C]//2014 IEEE International Conference on Image Processing (ICIP), October 27-30, 2014, Paris, France. New York: IEEE, 5357-5361(2014).

[36] Kang X G, Li Y X, Qu Z H et al. Enhancing source camera identification performance with a camera reference phase sensor pattern noise[J]. IEEE Transactions on Information Forensics and Security, 7, 393-402(2012).

[37] Kang X G, Li Y X, Qu Z H et al. Enhancing ROC performance of trustworthy camera source identification[J]. Proceedings of SPIE, 7880, 788009(2011).

[38] Lin X F, Li C T. Preprocessing reference sensor pattern noise via spectrum equalization[J]. IEEE Transactions on Information Forensics and Security, 11, 126-140(2016).

[39] Hsu C C, Hung T Y, Lin C W et al. Video forgery detection using correlation of noise residue. [C]//2008 IEEE 10th Workshop on Multimedia Signal Processing, October 8-10, 2008, Cairns, Qld, Australia. New York: IEEE, 170-174(2008).

[40] Chierchia G, Parrilli S, Poggi G et al. PRNU-based detection of small-size image forgeries. [C]//2011 17th International Conference on Digital Signal Processing (DSP), July 6-8, 2011, Corfu, Greece. New York: IEEE, 12216957(2011).

[41] Cozzolino D, Gragnaniello D. -11-27)[2020-02-16][EB/OL]. Verdoliva L. A novel framework for image forgery localization., org/abs/1311, 6932(2013). https://arxiv.

[42] Chierchia G, Poggi G, Sansone C et al. A Bayesian-MRF approach for PRNU-based image forgery detection[J]. IEEE Transactions on Information Forensics and Security, 9, 554-567(2014).

[43] Li C T, Li Y. Color-decoupled photo response non-uniformity for digital image forensics[J]. IEEE Transactions on Circuits and Systems for Video Technology, 22, 260-271(2012).

[44] Hu Y J, Yu B H, Jian C. Source camera identification using large components of sensor pattern noise. [C]//2009 2nd International Conference on Computer Science and its Applications, December 10-12, 2009, Jeju, Korea. New York: IEEE(2009).

[45] Tomioka Y, Ito Y, Kitazawa H. Robust digital camera identification based on pairwise magnitude relations of clustered sensor pattern noise[J]. IEEE Transactions on Information Forensics and Security, 8, 1986-1995(2013).

[46] Li R Z, Guan Y, Li C T. PCA-based denoising of Sensor Pattern Noise for source camera identification. [C]//2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), July 9-13, 2014, Xi’an, China. New York: IEEE, 436-440(2014).

[47] Bayram S, Sencar H T, Memon N. Efficient sensor fingerprint matching through fingerprint binarization[J]. IEEE Transactions on Information Forensics and Security, 7, 1404-1413(2012).

[48] Valsesia D, Coluccia G, Bianchi T et al. Compressed fingerprint matching and camera identification via random projections[J]. IEEE Transactions on Information Forensics and Security, 10, 1472-1485(2015).

[51] Gloe T, Böhme R. The Dresden image database for benchmarking digital image forensics[J]. Journal of Digital Forensic Practice, 3, 150-159(2010).

[52] Shullani D, Fontani M, Iuliani M et al. VISION: a video and image dataset for source identification[J]. EURASIP Journal on Information Security, 2017, 15(2017).

[53] Tian H W, Xiao Y H, Cao G et al. Daxing smartphone identification dataset[J]. IEEE Access, 7, 101046-101053(2019).

[54] Goljan M. Digital camera identification from images-estimating false acceptance probability[M]. //Kim H J, Katzenbeisser S, Ho A T S. Digital watermarking. Lecture notes in computer science. Berlin: Springer, 5450, 454-468(2009).

[55] Al-Ani M, Khelifi F. On the SPN estimation in image forensics: a systematic empirical evaluation[J]. IEEE Transactions on Information Forensics and Security, 12, 1067-1081(2017).