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![Page 1: Identifying Computer Graphics Using HSV Model And Statistical Moments Of Characteristic Functions Xiao Cai, Yuewen Wang.](https://reader035.fdocuments.in/reader035/viewer/2022062407/56649e035503460f94aee02d/html5/thumbnails/1.jpg)
Identifying Computer Graphics Using HSV Model
And Statistical Moments Of Characteristic Functions
Xiao Cai, Yuewen Wang
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Definition• Computer graphics, created by a variety of
rendering software (C.G.)
• Photographic images, the output of imaging acquisition devices such as the digital camera. (P.I.)
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Q: Can you tell me which two are the computer graphics and which two are the photographic image?
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outline
• Introduction
• Selection of color model & Image features
• Experiment
• Conclusion and future works
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Paper Objective and Problem Statements
This paper aims at the development of a novel method to automatically separate computer graphics from photographic images based on the following problem statements:
- The detection of computer graphics can be regarded as a two-class pattern recognition problem
- Feature-based methods are of our interest
- There should exist some appropriate features that are capable of distinguishing computer graphic images from photographic images with high accuracy
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The Breakthrough of This Paper
• On one hand, this technology will defeat the image forgery in the following areas: criminal investigation, journalism, etc.
• On the other hand, It will improve the rendering technology to generate more photorealistic computer graphics used in movie industry.
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The difference between C.G &Photographic images
• Computer graphics are more color smooth than photographic images in the texture area
• Fewer colors are contained in computer graphics
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Comparison • [1] T. Ianeva, A. de Vries and H. Rohrig “Detecting
cartoons: a case study in automatic video-genre classification,” (modeling the characteristics of cartoon, the dimensionality of features is 108)
• [2] S. Lyu and H. Farid, “How realistic is photorealistic?”(the first four order statistics and 3 directions subbands, the dimensionality of features is 216)
• [3] T.-T. Ng, S.-F. Chang, J. Hsu,L. Xie, and M.-P. Tsui, “physics-motivated features for distinguishing photographic images and computer graphics,”(the geometry features are characterized by differential geometry, fractal geometry and local patch statistics the dimensionality of features is 192)
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The representations of color images• RGB (Red, Green, Blue)
• HSV (Hue, Saturation, Value) Chromaticity BrightnessIn the HSV model, the luminous component
(brightness) is decoupled from color-carrying information (hue & saturation)
When viewing a color object, human visual system characterizes it brightness and chromaticity separately.
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HSV color model
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Moments of wavelet characteristic function
Specifically, we have shown that a statistical model based on first- and higher-order wavelet statistics reveals subtle but significant differences between photographic and photorealistic images.
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The construction of prediction-error image• The prediction-error image is generated by subtracting the
predicted image from the corresponding original image.• The prediction algorithm utilized to create the predicted
image is given in the followings
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Feature extraction• HSV-based features (compared to RGB-
based features)
• 234=78x3, 78=39x2, 39=13x3, 13=1+3x4
H,S,V or RGB
Original &predictio
n error
The first 3 moments
Original image
The first 3 levers of Haar wavelet
4 direction subbands
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Image database
• In this experiments, the author uses 1,900 photographic images (P.I.): 800 from the Columbia Image Dataset 400 from Philip Greenspun’s personal collection 700 from Google Image Search
800 computer graphics (C.G.): All from the Columbia Image Dataset
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SVM Classifier• The Support Vector Machine (SVM) classifier
with RBF (Radial Basis Function) kernel is employed in the two-class classification experiment;
• Use the “grid-search” method of LIBSVM to find the optimal penalty parameter C and kernel parameterγ of RBF kernel ;
• The number of iteration is 20;• Randomly select 5/6 of image set as the training
samples(1580 P.I. and 665 C.G.), and select the other 1/6 ,which are not involved in the training stage, as the testing samples(135 P.I. and 135 C.G.)
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Experimental Results• TP (true positive) represents the detection rate of computer
graphics ,while TN (true negative) represents the detection rate of photographic images.
• The accuracy =(135x TP+135x TN)/(135+135)=(TP+TN)/2• The best classification result using the optimal parameters for 3
components and for 2 components are shown in the follows
[2] 156-D features
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Results analysis• The accuracy is 82.1% for HSV-based features, which is 5.2% higher
than the accuracy of RGB-based features, which can indicate that the color model has an obvious influence on the effectiveness of image features.
• The proposed HSV-based features outperform the 216 wavelet features proposed in [2], which collects features in RGB space
• The 156 features from the hue and brightness components can achieve accuracy of 79.6%, which is better than the 234 RGB-based features and comparable to the 216 wavelet features[2]
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Conclusion and the future work• The distinguishing features are formed by using
statistical moments of characteristic function of wavelet subbands and their prediction-errors, whose effect has been investigated;
• The proper selection of color image representation is important to extract effective features;
• One of the works in the future is to design an optimization algorithm to search for the best color model;
• Another work is to use some methods, like boosting to select a reduced features set without significant degradation in classification performance.
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• Consider a two-class prediction problem (binary classification), in which the outcomes are labeled either as positive (p) or negative (n) class. There are four possible outcomes from a binary classifier. If the outcome from a prediction is p and the actual value is also p, then it is called a true positive (TP); however if the actual value is n then it is said a false positive (FP). Conversely, a true negative has occurred when both the prediction outcome and the actual value are n, and false negative is when the prediction outcome is n while the actual value is p.
• In signal detection theory, a receiver operating characteristic (ROC), or simply ROC curve, is a graphical plot for a binary classifier system as its discrimination threshold is varied. The ROC can also be represented equivalently by plotting the fraction of true positives (TPR = true positive rate) vs. the fraction of false positives (FPR = false positive rate).
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