Diploma Thesis: Automatic Discrimination of Photos and Computer Graphics Images.

1. Diploma Thesis:Automatic Discrimination of Photos and Computer Graphics Images. KonstantinosAnnousakis-Giannakopoulos Supervisor: Professor AthanassiosSkodras

2. Why the discrimination of CGI and photographs is useful? CGI Photograph vs • Photorealistic computer graphics images Difficult to discriminate if an image is CGI or photograph • Photograph = Reality • Use of Photograph as an evidence of truth in law courts and journalism • The integrity and reliability of photographs are nowadays questioned

3. Sometimes it is impossible even for a naked eye to discriminate if an image is a representation of a real scene or not. An example of two images from the two categories is illustrated below. CGI Photograph Images from the paper “E. Tokuda, H. Pedrini, and A. Rocha, “Computer generated images vs digital photographs: A synergetic feature and classifier combination approach,” Journal of Visual Communication and Image Representation, vol.24, pp. 1276-1292, Nov 2013.”

4. Characteristics ofCGI • Patches of uniform colors • Subtle color variation • Simple scenes • Small number of objects • They contain text • Additive noise to increase the levels of photorealism

5. Examples of the above mentioned CGI characteristics. Images from the database of the the paper “E. Tokuda, H. Pedrini, and A. Rocha, “Computer generated images vs digital photographs: A synergetic feature and classifier combination approach,” Journal of Visual Communication and Image Representation, vol.24, pp. 1276-1292, Nov 2013.”

6. The proposed method for discrimination The proposed method has its roots in JPEG compression, i.e. it utilizes the DCT of the 8x8 non-overlapping blocks of an image in the YCbCr color space. The features used by the proposed method are the first four statistical moments (mean, variance, skewness and kurtosis) of two sets of error signals, where the first set contains prediction errors for various DCT coefficients, while the second set considers errors in the spatial domain, at different compression scales.

7. Feature extraction process RGB ⇒YCbCr DCT DC&ACprediction 108 Coefficient statistics Image(512x512) DCprediction 12 Coefficient statistics DCprediction 12 Coefficient statistics DCprediction 12 Coefficient statistics

8. First Set of Features The first set of features is collected by working in the DCT domain, where nine linear predictors are used for various DCT coefficients and the first four statistical moments of the error signals are computed as features. Depending on what each predicted DCT coefficient represents, a different set of inputs is considered in the corresponding predictor. The inputs in the predictors are the DC values of the 3x3 block neighborhood.

9. DCT coefficients and the 3x3 neighborhood DCT basis functions 3x3 Neighborhood of8x8 block

10. Second Set of Features The second set of features is collected by working in the spatial domain, at different compression scales, where a predictor is used for quantized DC values and moments of the errors are collected as features.

11. Classification • SVM with Radial Basis Function(RBF) kernel • For the experiments we used 9700 Images including 4850 PH and 4850 CG from the database from the paper:“E. Tokuda, H. Pedrini, and A. Rocha, “Computer generated images vs digital photographs: A synergetic feature and classifier combination approach,” Journal of Visual Communication and Image Representation, vol.24, pp. 1276-1292, Nov 2013.” • Training/Testing set Ratio 80%. • 10 random Training/Testing set splits for reliability of the results

12. Images from the database used Photographs CGI

13. Experimental Results

14. Why three different QFs? Experiments with different sizes of central block