Source Camera Identification Forensics Based on Wavelet Features

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1 Source Camera Identification Forensics Based on Wavelet Features Bo Wang, Yiping Guo, Xiangwei Kong, Fanjie Meng, China IIH-MSP-29 September 13, 29

2 Outline Introduction Image features based identification Kharrazi s method Our method Experimental results and conclusions IIH-MSP-29

3 Introduction Source Camera Identification: Identifying the source camera of a digital photograph Used for: Establishing the origin of legal photographic evidence IIH-MSP-29

4 Active and Passive Identification Active Identification Embed watermarks No watermarks in most of digital photographics Passive Identification Do not need embed any information Only using image data Our method is a passive identification IIH-MSP-29

5 Using EXIF for Identification which one is its original EXIF? The left one. The EXIF of the right one is replaced by another image. IIH-MSP-29

6 Image Features Based Identification Imaging pipeline in digital cameras Differences in the processing details of each stage among various models of digital cameras Differences of image features in the output images from cameras of different models IIH-MSP-29

7 Kharrazi s Method Polytechnic University, Brooklyn, NY,USA: Mehdi Kharrazi, Husrev T. Sencar, Nasir Memon Using Pattern Recognition Image Features: color features,iqm features, mean of wavelet coefficients IIH-MSP-29

8 Can we do better? Shortage of Kharrazi s method Identification accuracy is not reliable Why? Image Features used are not effective What we do? Extract more effective features IIH-MSP-29

9 Features Extraction Features Selection Classification Our method IIH-MSP-29

10 Wavelet Features Higher-order wavelet statistics Statistics of linear prediction of wavelet coefficients A kind of filter operation in wavelet domain Less dependence on image content Wavelet Coefficient Co-occurrence statistics Distances of co-occurrence matrices in the same orientation between different scales Wavelet features Differences in impact of imaging pipelines on wavelet domain IIH-MSP-29

11 Higher-order Wavelet Features HL r 1,2,3, g, b HH r 1,2,3, g, b LH r 1,2,3, g, b log 2 ( ) log 2 ( ) ( ) ω1 ω2 ω3 g g g g V x, y = V ( x 1, y) + V ( x + 1, y) + V ( x, y 1) υ i i i i + ω V ( x, y + 1) + ω V ( x / 2, y / 2) + ω V ( x, y) g g g 4 i 5 i+ 1 6 i + g r b ω7 Di + 1( x / 2, y / 2) + ω8 Vi ( x, y) + ω9 Vi ( x, y) = Qω E ( ω ) = [ υ Qω ] 2 de( ω) T 2 Q ( υ Qω ) dω = 1 ( T T ω = Q Q) Q υ p = log ( υ ) log ( Qω ) IIH-MSP-29

12 Wavelet Coefficient Co-occurrence Statistics HH r 1,2,3, g, b LH r HL r 1,2,3, g, b 1,2,3, g, b c c c DC( Vi ) = CVi CVi+ 1 c c c DC( H i ) = CH i CH i+ 1 c c c DC( Di ) = CDi CDi + 1 c c c CV i CH i CD : vertical, horizontal, and i diagonal subbands co-occurrence matrices i = 1,2,3,4 c = r, g, b [ j] 2 Contrast = ( i j) DC i, Homogeneity = Correlation = = i j i j DC( i, j) i j 1+ i j i j ( i µ [ i j] 2 Energy DC, = i j [ j] log DC[ i j] Entropy DC i, i, 2 )( j µ σ σ i j j ) DC i [, j] IIH-MSP-29

13 Feature Selection and Classification Sequential Forward Feature Selection (SFFS) Support Vector Machine (SVM) C-support vector classification with non-linear RBF kernel IIH-MSP-29

14 Experiment Experiment samples and parameters Cameras Sensor Camera Parameters Max resolution Sample image parameters Image resolution Image format Kodak DC29 Unspecified CCD 224*15 224*15 JPEG Nikon E57 2/3 inch CCD 256*192 16*12 JPEG Sony DSC-F828 2/3 inch CCD 3264* *96 JPEG Canon PowerShot Pro1 2/3 inch CCD 3264* *768 JPEG Canon PowerShot G2 1/1.8 inch CCD 2272* *768 16* *174 JPEG Canon PowerShot G3 1/1.8 inch CCD 2272* *174 JPEG IIH-MSP-29

15 Experiment result of our method Confusion matrix Camera Kodak Nikon Sony CanonPro1 CanonG2 CanonG3 Accuracy Kodak DC % Nikon % Sony DSC-F % Canon PowerShot Pro % Canon PowerShot G % Canon PowerShot G % IIH-MSP-29

16 Comparison with Kharrazi s method Camera Kodak Nikon Sony CanonPro1 CanonG2 CanonG3 Accuracy Kharrazi s method 94.7% 91.3% 96.3% 85.3% 84.7% 93.3% 9.9% Our method 1% 98.7% 98.7% 96.7% 11.4% 95.3% 1.6% 98.7% 5.4% 98.2% IIH-MSP-29

17 Conclusions 1. Introduce feature based source camera identification 2. Discuss a classic feature based identification method 3. Give a new source camera identification method based on wavelet features IIH-MSP-29

18 Thank you! IIH-MSP-29

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