Multi-scale noise estimation for image splicing forgery detection

A novel multi-scale noise estimation method is proposed for splicing detection.Noise level function is used to separate potential spliced segments.An Optimal Parameter Combination Searching Algorithm is proposed. Noise discrepancies in multiple scales are utilized as indicators for image splicing forgery detection in this paper. Specifically, the test image is initially segmented into superpixels of multiple scales. In each individual scale, noise level function, which reflects the relation between noise level and brightness of each segment, is computed. Those segments not constrained by the noise level function are regarded as suspicious regions. In the final step, pixels appears in suspicious regions of each scale, after necessary morphological processing, are marked as spliced region(s). The Optimal Parameter Combination Searching (OPCS) Algorithm is proposed to determine the optimal parameters during the process. Two datasets are created for training the optimal parameters and to evaluate the proposed scheme, respectively. The experimental results show that the proposed scheme is effective, especially for the multi-objects splicing. In addition, the proposed scheme is proven to be superior to the existing state-of-the-art method.

[1]  Babak Mahdian,et al.  Ieee Transactions on Information Forensics and Security 1 Blind Authentication Using Periodic Properties of Interpolation , 2022 .

[2]  Alessandro Piva,et al.  Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[3]  P. Lancaster.  Curve and surface fitting , 1986 .

[4]  Lei Zheng,et al.  Image Noise Level Estimation by Principal Component Analysis , 2013, IEEE Transactions on Image Processing.

[5]  Xing Zhang,et al.  Exposing image splicing with inconsistent local noise variances , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

[6]  Hany Farid,et al.  Exposing digital forgeries through chromatic aberration , 2006, MM&Sec '06.

[7]  Takeo Kanade,et al.  Statistical calibration of CCD imaging process , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Xiaochun Cao,et al.  Identifying Image Composites Through Shadow Matte Consistency , 2011, IEEE Transactions on Information Forensics and Security.

[9]  M. Bethge.  Factorial coding of natural images: how effective are linear models in removing higher-order dependencies? , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Shih-Fu Chang,et al.  Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[11]  Chang-Tsun Li,et al.  Digital camera identification using Colour-Decoupled photo response non-uniformity noise pattern , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[12]  Richard Szeliski,et al.  Automatic Estimation and Removal of Noise from a Single Image , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Xiaochun Cao,et al.  Forgery Authentication in Extreme Wide-Angle Lens Using Distortion Cue and Fake Saliency Map , 2012, IEEE Transactions on Information Forensics and Security.

[14]  Eero P. Simoncelli,et al.  Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization , 2009, Neural Computation.

[15]  Yao Zhao,et al.  Forensic detection of noise addition in digital images , 2014, J. Electronic Imaging.

[16]  Asoke K. Nandi,et al.  Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics , 2011, Signal Process..

[17]  Xing Zhang,et al.  Exposing image forgery with blind noise estimation , 2011, MM&Sec '11.

[18]  Xing Zhang,et al.  Exposing Region Splicing Forgeries with Blind Local Noise Estimation , 2013, International Journal of Computer Vision.

[19]  Shih-Fu Chang,et al.  Camera Response Functions for Image Forensics: An Automatic Algorithm for Splicing Detection , 2010, IEEE Transactions on Information Forensics and Security.

[20]  Alessandro Piva,et al.  Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[21]  Guna Seetharaman,et al.  Harnessing Motion Blur to Unveil Splicing , 2014, IEEE Transactions on Information Forensics and Security.

[22]  Yao Zhao,et al.  Contrast Enhancement-Based Forensics in Digital Images , 2014, IEEE Transactions on Information Forensics and Security.

[23]  Hany Farid,et al.  Exposing Digital Forgeries in Complex Lighting Environments , 2007, IEEE Transactions on Information Forensics and Security.

[24]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.

[25]  Stefano Tubaro,et al.  Revealing the Traces of JPEG Compression Anti-Forensics , 2013, IEEE Transactions on Information Forensics and Security.

[26]  Christian Riess,et al.  Exposing Digital Image Forgeries by Illumination Color Classification , 2013, IEEE Transactions on Information Forensics and Security.

[27]  Shinfeng D. Lin,et al.  An integrated technique for splicing and copy-move forgery image detection , 2011, 2011 4th International Congress on Image and Signal Processing.

[28]  Nenghai Yu,et al.  Passive detection of doctored JPEG image via block artifact grid extraction , 2009, Signal Process..

[29]  Yair Weiss,et al.  Scale invariance and noise in natural images , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Mo Chen,et al.  Source digital camcorder identification using sensor photo response non-uniformity , 2007, Electronic Imaging.

[32]  Alex ChiChung Kot,et al.  Blurred Image Splicing Localization by Exposing Blur Type Inconsistency , 2015, IEEE Transactions on Information Forensics and Security.

[33]  Hany Farid,et al.  Exposing Digital Forgeries From JPEG Ghosts , 2009, IEEE Transactions on Information Forensics and Security.

[34]  Glenn Healey,et al.  Radiometric CCD camera calibration and noise estimation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Xinpeng Zhang,et al.  Estimation of Image Rotation Angle Using Interpolation-Related Spectral Signatures With Application to Blind Detection of Image Forgery , 2010, IEEE Transactions on Information Forensics and Security.