Image Tampering Detection

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1 Image Tampering Detection Yun Q. Shi and Patchara Sutthiwan Intelligent Multimedia Lab New Jersey Institute of Technology 24 April 2009 A Natural Image Model Approach to Tampering Detection Acknowledgement The contributions made by Professor Xuan, Drs. Zou, Fu, C. Chen, W. Chen, Su are deeply appreciated. 2 1

2 Machine Learning Framework A two- or multiple-class pattern recognition problem. Input Image Feature Extractor y i Pattern Classifier Recognized Class ω j y i = R n is a feature vector and its associated class is ω j {- 1,1}.ω i = 1 if y i is image, otherwise ω j =-1. The classifier learns the mapping y i ω j in the training stage and produces a decision function. Popular classifiers include Bayes, support vector machine (SVM) and neural network (NN). 3 Merits of Proposed Model Two combinations: Combination of features derived from the image pixel 2-D array, and those derived from the Multiblock DCT (MBDCT) coefficient 2-D arrays Combination of moments of characteristic functions based features and Markov process based features These features can make up each other when properly applied. 4 2

3 Block Diagram of the Natural Image Model Given Image (Pixel 2-D array) 2 2 Block Discrete Cosine Transform n n Block Discrete Cosine Transform N N Block Discrete Cosine Transform MBDCT 2 2 BDCT Coefficient 2-D Array n n BDCT Coefficient 2-D Array N N BDCT Coefficient 2-D Array Moment Features Moment Features Moment Features Moment Features Moment Features Markov Features Markov Features Markov Features Markov Features Markov Features 5 Moment Feature Generation 2-D Array Predictionerror 2-D array Generation Horizontal 2-D Histogram Vertical 2-D Histogram Main Diagonal 2-D Histogram LL 0 DWT subbands Histogram DFT Moments LL 0 DWT subbands Histogram DFT Moments 2-D DFT 2-D DFT 2-D DFT Minor Diagonal 2-D Histogram 2-D DFT Marginal moments Marginal Moments Marginal Moments Marginal Moments 6 3

4 Markov Feature Generation Horizontal Difference 2-D Array T -T Transition Probability Matrix (2T+1) 2 -D Image Pixel 2-D Array or BDCT Coefficient 2-D Array Vertical Difference 2-D Array Main Diagonal Difference 2-D Array T -T T -T Transition Probability Matrix Transition Probability Matrix (2T+1) 2 -D (2T+1) 2 -D Minor Diagonal Difference 2-D Array T -T Transition Probability Matrix (2T+1) 2 -D 7 A Concrete Implementation The main concern is the limited number of images in [1]. Block sizes are selected as 2 2, 4 4, and 8 8. For moment features, One-level Haar wavelet decomposition and horizontal and vertical 2-D histograms only. When computing Markov features, we only apply Markov process to horizontal and vertical difference 2-D array of 8 8 BDCT coefficient 2-D array and threshold T = 4. Feature dimensionality: = 330. SVM is employed as the classifier. 8 4

Block Diagram of the Implementation Given Image (Pixel 2-D array) Moment Feature Extraction 42-D 2 2 Block Discrete Cosine Transform 2 2 BDCT Coefficient 2-D Array Moment Feature Extraction 42-D 4 4

5 Block Diagram of the Implementation Given Image (Pixel 2-D array) Moment Feature Extraction 42-D 2 2 Block Discrete Cosine Transform 2 2 BDCT Coefficient 2-D Array Moment Feature Extraction 42-D 4 4 Block Discrete Cosine Transform 4 4 BDCT Coefficient 2-D Array Moment Feature Extraction 42-D 8 8 Block Discrete Cosine Transform 8 8 BDCT Coefficient 2-D Array Moment Feature Extraction 42-D Markov Feature Extraction 162-D 9 Examples of spliced images (Columbia dataset) 10 5

7 Experimental results on Columbia dataset (I) On Columbia Dataset Dartmouth Columbia NJIT-1 NJIT-2 NJIT-3 73% 71% 80% 82% 91% randomly selected images from Columbia dataset 14 7

8 10 randomly selected spliced images from Columbia dataset 15 Experimental results on real image tampering cases (II) three well-known news examples Kerry and Fonda (2004) success Israel air raid Lebanon (2006) success Iraqi solder (2003) half success half failure 16 8

9 Kerry and Fonda (2004) Picture Actual type Kerry Authentic 20/20 0/20 Fonda Authentic 15/20 5/20 Kerry & Fonda Tampered 0/20 20/20 17 Israel Air Raid Lebanon (2006) Picture Actual type On the left Authentic 20/20 0/20 On the right Tampered 0/20 20/

US Soldier in Iraqi (2003) Picture Actual type On the left Authentic

0/20 20/20 19 Experimental results on real image tampering cases

Israel success ABC News lady success Rove success Fire on Ice

10 US Soldier in Iraqi (2003) Picture Actual type On the left Authentic 0/20 20/20 In the middle Authentic 0/20 20/20 On the right Tampered 0/20 20/20 19 Experimental results on real image tampering cases (III) Girl (Air Force Research Lab) success Stalin success April 09, Israel success ABC News lady success Rove success Fire on Ice success Martha, Newsweek success Pitt and Julia success Reagan success 20 10

11 Girl (Air Force Research Lab) Picture Actual type On the left Authentic 13/20 7/20 On the right Tampered 2/20 18/20 21 Stalin Picture Actual type On the left Authentic 19/20 1/20 On the right Tampered 7/20 13/

12 April 09, Israel 23 April 09, Israel Picture Actual type On the left Authentic 15/20 5/20 On the right Tampered 0/20 20/

13 ABC News Lady Actual type Tampered 7/20 13/20 25 Karl Rove 26 13

14 Karl Rove Actual type Tampered 0/20 20/20 27 Fire on Ice Actual type Tampered 0/20 20/

15 Martha Actual type Tampered 0/20 20/20 29 Brad and Angelina 30 15

16 Brad and Angelina Actual type Tampered 0/20 20/20 31 Reagan Picture Actual type On the left Tampered 6/20 14/20 On the right Tampered 1/20 19/

Experimental results on real image tampering cases (IV) Tibet Antelope success Hitler failure (analogue, other processing) Iranian missile failure (size, other factor)

17 Experimental results on real image tampering cases (IV) Tibet Antelope success Hitler failure (analogue, other processing) Iranian missile failure (size, other factor) Poster and soldiers success (size) Nonsufficient training Un-known factors Head-shoulder (some success, some failure) 33 Tibet Antelope Feb. 25, 2008 (Chinese News Agency) 34 17

18 Tibet Antelope Feb. 25, 2008 (Chinese News Agency) Actual type Tampered 2/20 18/20 35 Hitler 36 18

19 Hitler Picture Actual type On the left Authentic 4/20 16/20 On the right Tampered 20/20 0/20 37 Iranian Missiles 38 19

20 Iranian Missiles Picture Actual type On the left Authentic 0/20 20/20 On the right Tampered 0/20 20/20 39 Poster and Soldier 40 20

21 Poster and Soldier Actual type Tampered 0/20 20/20 41 Experimental results on real image tampering cases (V) Two images from Nick 1st: Daisy 42 21

22 2 nd : Daisy and Ivy 43 Picture Actual type On the left Authentic 20/20 0/20 On the right Tampered 0/20 20/

23 45 Picture Actual type On the left Tampered 0/20 20/20 On the right Tampered 7/20 13/

24 Classification Results from Columbia Dataset Filename Type blk7 20/20 0/20 blk36 20/20 0/20 blk_3_63 20/20 0/20 blk_4_46 20/20 0/20 blk_5_28 20/20 0/20 blk_6_1 20/20 0/20 blk_8_74 20/20 0/20 blk_9_12 20/20 0/20 blk_10_21 20/20 0/20 blk_11_44 20/20 0/20 47 blk_7_s 12 8 blk_12_ blk_13_ blk_14_ blk_14_ blk_14_ blk_14_ blk_16_ blk_17_ blk_17_

25 Classification Results from Real Image Tampering Cases Description Filename Actual Type Authentic by (per 20 classifiers) Tempered/Spliced by (per 20 classifieres) Fonda 4a.bmp 15 5 Kerry 5a.bmp 20 0 Fonda and Kerry Combined 6t.bmp 0 20 Israel air raid Lebanon 7a.bmp t.bmp 0 20 tibetgoat_crop Tibet Goat ped.bmp 2 18 Stalin (cropped with 256x235) stalin_a.bmp 19 1 stalin_t.bmp April 09, Israel y_a.bmp 15 5 y_t.bmp 0 20 solder (cropped with 256x256 at the messages) solder_t.bmp 0 20 Martha on Newsweek cover martha_t.bmp 0 20 ABC News abc_news_t.bmp 7 13 Rove rovekarl_t.bmp 7 20 April 09, Detrich's basketball_small_t.b Photo, ball added mp 0 20 Mussolini mussolini_a.bmp 14 6 mussolini_t.bmp 20 0 Black girl IF9905_5.jpg 13 7 IF9905_7.jpg

26 fire on ice fireonice_t.bmp 0 20 Pitt (cropped with 193x132) pitt_t.bmp 0 20 Regan on Times (200x244 full size) reagan.jpg 6 14 (cropped with 128x192) reagan_mod_t.bmp 1 19 Iran Missile iran_missile_a.bmp 0 20 iran_missile_t.bmp 0 20 Hitler hitler_a.bmp 4 16 hitler_t.bmp 20 0 Lena IF9905_9.jpg 20 0 Castro IF9905_10.jpg 20 0 castro_a.bmp (256x256) 18 2 castro_t.bmp (256x256) castro_t2.bmp (368x278) 16 4 castro_t3.bmp (256x256) References Data set of and spliced image blocks, DVMM, Columbia University, magevideoforensic/ H. Farid: High-order statistics and their applications to digital forensics, IEEE Workshop on Statistical Analysis in Computer Vision (in conjunction with CVPR), T.-T. Ng, S.-F. Chang and Q. Sun, Blind detection of photomontage using higher order statistics, IEEE ISCAS04, May, D. Fu, Y. Q. Shi, and W. Su: Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition, IWDW2006. W. Chen, Y. Q. Shi, and W. Su: Image splicing detection using 2-D phase congruency and statistical moments of characteristic function, SPIE2007. Y. Q. Shi, C. Chen, W. Chen, A natural image model approach to splicing detection, ACM Workshop on Multimedia Security, Dallas, Texas, September

Steganalysis versus Splicing Detection

Steganalysis versus Splicing Detection Yun Q. Shi 1, Chunhua Chen 1, Guorong Xuan 2, and Wei Su 1 1 New Jersey Institute of Technology, University Heights, Newark, NJ, USA 07102 2 Tongji University, Shanghai,