Forensic detection of inverse tone mapping in HDR images

High dynamic range (HDR) imaging is attracting an increasing deal of attention in the multimedia community, yet its forensic problems have been little studied so far. This paper proposes an HDR image forensic method, which aims at differentiating HDR images created from multiple low dynamic range (LDR) images from those created from a single LDR image by inverse tone mapping. For each kind of HDR image, a Gaussian mixture model is learned. Thereafter, an HDR image forensic feature is constructed based on calculating the Fisher scores. With comparison to a steganalytic feature and a texture/facial analysis feature, experimental results demonstrate the efficiency of the proposed method in HDR image forensic classification on whole images as well as small blocks, for three inverse tone mapping methods.

[1]  A. Piva An Overview on Image Forensics , 2013 .

[2]  Erik Reinhard,et al.  Photographic tone reproduction for digital images , 2002, ACM Trans. Graph..

[3]  Mauro Barni,et al.  Second-Order Statistics Analysis to Cope With Contrast Enhancement Counter-Forensics , 2015, IEEE Signal Processing Letters.

[4]  Johann A. Briffa,et al.  Image Forensics of High Dynamic Range Imaging , 2011, IWDW.

[5]  Zhang Xiong,et al.  JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics , 2013, IH&MMSec '13.

[6]  Jiwu Huang,et al.  A universal image forensic strategy based on steganalytic model , 2014, IH&MMSec '14.

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Gaurav Sharma,et al.  Local Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis , 2012, ECCV.

[9]  Tomás Pevný,et al.  Steganalysis by Subtractive Pixel Adjacency Matrix , 2009, IEEE Transactions on Information Forensics and Security.

[10]  David Haussler,et al.  Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.

[11]  Tomás Pevný,et al.  Steganalysis by subtractive pixel adjacency matrix , 2010, IEEE Trans. Inf. Forensics Secur..

[12]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[13]  Erik Reinhard,et al.  Do HDR displays support LDR content?: a psychophysical evaluation , 2007, ACM Trans. Graph..

[14]  Mark D. Fairchild,et al.  The HDR Photographic Survey , 2007, CIC.

[15]  Manuel Menezes de Oliveira Neto,et al.  High-Quality Reverse Tone Mapping for a Wide Range of Exposures , 2014, 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images.

[16]  Gaurav Sharma,et al.  Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns , 2015, Comput. Vis. Image Underst..

[17]  Yair Weiss,et al.  "Natural Images, Gaussian Mixtures and Dead Leaves" , 2012, NIPS.

[18]  Wolfgang Heidrich,et al.  High dynamic range display systems , 2004, ACM Trans. Graph..

[19]  Feng Xiao,et al.  High Dynamic Range Imaging of Natural Scenes , 2002, CIC.

[20]  Fan Yang,et al.  Physiological inverse tone mapping based on retina response , 2013, The Visual Computer.

[21]  Giuseppe Valenzise,et al.  Dynamic range expansion of video sequences: A subjective quality assessment study , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[22]  Kai Wang,et al.  General-purpose image forensics using patch likelihood under image statistical models , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

[23]  Karol Myszkowski,et al.  High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content , 2009, Eurographics.

[24]  Sylvain Paris,et al.  Learning photographic global tonal adjustment with a database of input / output image pairs , 2011, CVPR 2011.

[25]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.