Badness Information Audit Based on Image Character Filtering* Fei Yu1,2, Hunag Huang1, Cheng Xu3, Xiao-peng Dai1, and Miao-liang Zhu2 1
Institute of Computer & Information Engineering, Hunan Agricultural University, Changsha 410128, Hunan, China {yufei,huangh,dxp}@hunau.net 2 Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, Zhejiang, China
[email protected] 3 College of Computer & Communication, Hunan University, Changsha 410082, Hunan, China
[email protected]
Abstract. Along with high development of multimedia information technique, the provider of badness information embeds some badness information to image or directly saves as a image file, avoiding the filter of image, which brings extreme effect of security hidden trouble in society. An information audit system based on image content filtering is provided in this paper. At first, we discuss some basic method filtering physical badness image content, analyze some key technology of filtering image content, and mark as texture character by four eigenvectors: contrast, energy, entropy and correlation. Afterwards, we utilize dynamic programming method to segment image objects, and utilize similarity measurement to denote similarity degree of two character measures. At last, we give an example of identify yellow content, which distill the texture character of image and match it with defined character database. Our system can supervise and control badness information of physical badness image content, and realize automation audit of multimedia information.
1 Introduction At present, network information audit system realizes information filtering function mostly through capturing and analyzing text information[1]. Comparing with network filtering technique that depends purely on IP address and URL access control list, based text information filtering technique may filter real-time badness in the network, such as network information in some e-mail, chat-room etc[2]. But then, this technique exists itself in obvious limitation: some badness information providers transfer their badness information which are embedded to another image file or formed directly as image file, in order to avoid audit of network information audit system. Along with the development of multimedia technology and obviously advance of network band*
Foundation item:Supported by Hunan Provincial Natural Science Foundation of China(03JJY3103).
G. Chen et al. (Eds.): ISPA Workshops 2005, LNCS 3759, pp. 647 – 656, 2005. © Springer-Verlag Berlin Heidelberg 2005
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width, image and video information increase more and more in the Internet. Pictures added newly to the Internet each year have exceeded 80 billion pages in which there have respectable harmful information[3]. A research in Carnegie Mellon University shows that there has 83.5% picture information contain pornographic content stored in USENET newsgroup. It is a non-disputed fact that there has a great deal of badness information spread in the Internet. Thereby, it is very necessary to audit image content in the network.
2 Key Technology The content of image is the semantic information contained in the image. From the point of view of image processing, the image character can be divided into the basic empty area character and transformation character. Basic character by which image content filtering is used include as follows: Color texture character, order and edge shape and contour etc. The transformation character is a new characteristic obtained from every kind of transformation through basic character. Commonly used transformations contain the Fourior transformation, the K- L transformation, the Hough transformation and the Wavelet transformation[4]. Relative to another character, color character is consistently dependable, which is not sensitive to circumrotate image transfer image flatly change measure of image, indeed every kind of form transformation, and has quite robustness property. And that it’s computation is simple, thus the character is applied by widely. Typical methods of classification based on image color character have histogram cross method and histogram distance method, which makes use of the relationship between color and its probability appeared in the image to show whole color information of image.
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2.1 Basic Method Filtering Physical Badness Image Content Making use of this characteristic which is the tightness degree of aggregation region of complexion in color space, we establish a model for complexion, which can implement the detection of skin. There have three types of skin detection method: 1) experimental threshold method This method mainly utilizes the aggregation character of skin color, after removing brightness interference from skin, adopts adaptive color space as possible as to compress the distribution of skin, and sets a threshold value to lay off definite range, moreover according to this means, it can judge that there is a skin image content when the character value locates in this threshold range, otherwise there is a background image content. Though this method is a simple and advantageous computation method, it depends on sample database excessively. It is very difficult to identify skin image content exactly, because that this method have not mathematical model of skin, and entirely via by test to stat distribution region, and then it has bigger chanciness.
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2) Bayes decision method Jones stats directly vertical square in RGB color space, and thus can obtain conditional probability distributions as p(rgb|skin) p(rgb|non-skin) p(skin) p(nonskin). And then we can compute out p(skin|rgb) by Bayes formula, which can set threshold value (commonly there is [0,1]) to identify skin image content. In a word, Jones utilizes Bayes method to make a decision, which increases its expansibility and adaptability to some extent. 3) Gaussian model method It has a obvious wave crest in the distribution of skin color in the chroma space. And the distribution of skin color in the chroma space can be marked as Gaussian distribution, which is one of familiar complexion model. Gaussian model parameters are not complex because of its mathematical denotation. And then we only need those parameters from stat. sample in its realization process. And that model parameters can be readjusted along with the update of sample.
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2.2 Denotation of Image Character Texture character is widely adopted by filtering badness image information. Character measures distilled usually include the degree of uniformity contrast and direction etc[5]. The degree of uniformity reflects the size of texture, contrast reflects definition of texture, direction reflects whether the entity have regular orientation or not. The method of distilling image texture character contain commonly Symbiosis Matrix Method K-L Transformation Texture Spectral Analysis and so on which are based on classic mathematics model, and which bring forward presently Multi-Resolution Analysis Gabor Filter Wavelet Analysis and so on which are based on vision model. We use four character measures to denote texture character which are shown as follows: 1) Contrast (also called principal diagonal moment of inertia):
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CON = ∑∑ (h − k ) 2 mhk h
(1)
k
For thick texture, because mhk value near in main diagonal, and (h-k) is small, the relative CON is also small. On the contrary, relative CON is big for thin texture. 2) Energy (also called moment of angle second order):
ASM = ∑ h
∑
(mhk ) 2
(2)
k
This is a measurement about distributing uniformity of image intensity. When mhk value near in main diagonal, the relative ASM is big; On the contrary, ASM value is small. 3) Entropy:
ENT = −∑ h
∑ k
mhk log mkh
(3)
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In symbiosis matrix of intensity, when mhk value distribute loosely, the relative ENT is big; On the contrary, mhk value distribute centralized and tightly, and ENT value is small. 4) Correlation:
⎡ ⎤ COR = ⎢ ∑∑ hkmhk − µ x µ y ⎥ / σ xσ y ⎣ h k ⎦ Where
(4)
u x , u y , σ x , σ y is respectively mean and standard variance of mx , my ,
mx = ∑ hk is the sum of value of all column element, my = ∑ hk is the sum of h
h
value of all row element. Correlation is described as the degree of similarity between row element and column element, which is a measure of the degree of linear relationship of intensity. 2.3 Stablishment of Character Library Character data in character library describe property of image object filtered, which is also warranty of classification for those images that the information audit system have captured, thus the design and implement of character library will directly impact system performance. By way of getting character data of such kind of image, we collect commonly certain relative kind of image as the sample images, and the amount of sample images must be wealthy and representative. And then we can get sample data through distilling the character of sample image, and use these sample data, apply to certain sample training algorithm, at last, can obtain basic character data of relative kind of image object. At practice, character data in character library would be updated at a period of time or be supplied continuously and dynamically through learning and training these sample images. Character data are commonly vector in high-dimension character space, in which the dimension of vector is decided by the select and distill process of image character. Moreover, the number of vectors in character library is decided together by the varieties of image filtering filtering precision and processing speed. In order to improve efficiency of retrieval, there would decrease the number of character vectors as possible as under the condition of keeping the same filtering precision. At the same purpose, character data of different kind of image store in different character library, so that there can realize filtering function of different kind of image object through selecting different character library[6].
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2.4 Segmentation of Image Object From the segmentation process of image object, it mostly identifies and locates image object, and distills the contour of image object[7]. The identification and location of image object confirms of the probable position of image object and knows from another object. Moreover, the distilling of object contour makes certain accurately the edges of image object, and draws the outline of image object. The process is shown in Fig 1.
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The segmentation method of image object include mostly Flood Fill Active Contour based on Snake and alternating Dynamic Programming. Dynamic Programming Method consider that pels array of image constitutes a directed graph, in which each pels in the image correspond to a point in the directed graph, and two neighboring pels compose of a side in the directed graph. Each side is set a value of character measure according as the edge character of image that is called weight in the side. In the segmentation process of image object, the edge detection of image object is namely how to look for a shortest route from this weight directed graph, and uses Dynamic Programming Method to compute this route.
Fig. 1. Distilling of object contour
We use alternating Dynamic Programming Method to realize the segmentation of image object, and the procedure describes as follows: 1. Define the directed graph of image; 2. Define the weight in each side; 3. Select the edge character of image and translate into weight in each side. Compute the shortest route between start point and end point confirmed in directed graph. 2.5 Network Data Collection The network processor is a special CPU to process network data[8], and be designed for optimizing the process of data packet, which transmits the packets to the next node at the arrived speed. The network processor is composed of network processor unit and appropriative coprocessor unit. The network processor unit is the kernel of network processor. It can intelligently process large capacity data at a high speed, such as data analysis, classification and forwarding etc. So network processor unit is also called data packet process engine. Different coprocessors have the functions of frame subsection/recombination, accelerate sort, queue/ buffer management, sequence management, memory control and so on. IXP 1200 is composed of 7 RISC processors, secondary storage interface. IX bus interface and PCI bus interface. In the 7 RISC
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processor, 6 are the packet processing engines and the rest one, called “StrongARM”, is used to manage/control the pack processing engine. IXF1002 has 2 full duplex Gbytes MAC interfaces. The speed of data collection can reach 5.12Gbps. IXF440 has 8 full duplex 10/100M MAC interface. IXF1002 and IXF440 are connected with IXP1200 by a IX bus. They transmit the collected data packets to network processor through the IX bus. 2.6 Similarity Calculation In this paper, the formula that is used to calculate similarity of image character describes as follows: n
S( X ,Y ) =
∑ (x × y ) i
i =1
i
(5)
n
n
∑ x ×∑ y 2
i =1
i
2 i
i=1
When similarity denotes similar degree of two character measures, similarity value is very big to mean that two character measures are more similar.
3 Skin Identification Sample point of skin and non-skin overlap in Character space, and it can't be divided into two independent parts completely, but the distribution of skin sample point accord with basically Gaussian distribution from the point of view of each dimensionality[9]. 3.1 Modeling of Skin Identification Therefore, we adopt Bayes Statistics Model to classify skin and non-skin in the image. These formulae describes as follows: P(O | S ) =
P( S | O) P(O ) P( S | O) P(O) + P ( S | B ) P( B )
(6)
P(S | O) = P(s1 | O)P(s2 | O)P(s3 | O)P(s4 | O)P(s5 | O)P(s6 | O)
(7)
P(S | B) = P(s1 | B)P(s2 | B)P(s3 | B)P(s4 | B)P(s5 | B)P(s6 | B)
(8)
Where, O is skin, B is non-skin, S is six dimension character vector. We suppose that
(
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is Gaussian distribution, and the six character vectors are independent P s⏐Ο i random variables one another. So, Gaussian distribution of skin and non-skin at each dimension can describe as follows:
P ( si | O ) =
1 2π σ o
−
e
( si − µ o ) 2 2σ o 2
(9)
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P ( si | B ) =
1 2π σ B
−
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( si − µ B ) 2
e
2σ B 2
(9)
µ 0 and σ 0 is mean and variance of sample of skin respectively at each dimension, µ B and σ B is mean and variance of sample of non-skin respectively at each Where
dimension, i is the number of dimensionality. 3.2 Experimentation of Skin Identification
In this research, four datasets are used to build Pornocide. The color histograms are established by 339 images with skin pixels and 16376 images without skin pixels. Pornocide is trained with the second dataset, which includes 999 pornographic images and 1203 non-pornographic images. The third and the fourth dataset are used to prove the generalization of Pornocide. The third dataset is consisted of 559 benign images and 559 pornographic images. There are 109 benign images and 49 pornographic images in the last dataset. The images in the first three dataset are extracted from images downloaded during a random crawl of the Internet or obtained from “Corel Gallery”. The last dataset is obtained from Baltimore Technologies. In paper[10], the counter of each bin stores the number of times that color value occurred in the entire database of images. In Pornocide, we make the contribution made by each images the same. It means that the contribution of one pixel in “a” image is different from one in “b” image. It depends on the number of pixels extracted from an image. More pixels one image has less weight its pixel represents. We constructed skin and non-skin histogram models using our 339 and 16376 image dataset. The skin pixels in the 339 images containing skin pixels were extracted manually and placed into the skin histogram. The 16376 images that did not contain skin pixels were placed into the non-skin histogram. After collecting pixels into the histograms, we employ the distribution of the histogram in people detection. Instead of conventional methods such as paper [10], we propose a novel approach to make use of the distribution. The approach is depicted as follows: First, we define some terms used in the following section. Ps (i): the probability of the ith bin in the skin histogram. Pns(i): the probability of the ith bin in the non-skin histogram. β(i): skin-to-nonskin factor, equals to Ps(i) /Pns(i). w(β): weighting function depends on β, the skin-to-nonskin factor. Second, we construct the new histogram derived from the weighting functions. Third, we apply the histogram to determine if there is skin in the images of the dataset. After numerous trials on the dataset, we find the optimal θ when the product of sensitivity and specificity is maximized. Finally, we exploit the histogram and θ to make the skin tone filter. Skin in the image is a very essential indication that the image is pornographic. It means that if the image is pornographic or objectionable, the image must be composed of skin color. As a result, in Pornocide, skin tone filter is the first mechanism used to detect pornographic image. We believe that a good skin tone filter could be a
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reliable and preliminary tool for filtering the pornographic images. In other words, it can get rid of most benign images, like scene or landscape, in this stage without difficulty. In this section, we are going to show how effective skin tone filter alone can be in this task.
. Fig. 2. Skin Tone Filter’s Performance
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3.3 Analysis of Experiment Result
From above figure, we can find out that statistical probability value of the mass skin sample point nears to in 1, rather than statistical probability value of the mass nonskin sample point nears to in 0,and distributes loose relatively. Bayes probability skin color model can identify the skin part of image effectively, even as shown as fig 2, provides the relative basis for filtering the pornographic picture. The results show that simply probing color values allows reasonably good classification of images into those being pornographic and those not. According to the experimental results, ρ is chosen to maximize the product of sensitivity and specificity (the product of sensitivity (95.5%) and specificity (90.1%) is 86.0%) to be the criterion to evaluate whether the image is pornographic or not. If there are more than 8.1% skin region in the image, the image will be passed to the next stage for advanced exam. Otherwise, the image will be thought as a benign image and leave the system.
4 System Architecture Image monitoring based on image content may divide into two steps: one is offline establishing image character library, the other is online information retrieval of image[11]. Information retrieval consists of server and client part. Server part consists of library subsystem and query engine module, client part finishes how to capture image file in the network and submit this file to server for matching image, and audits manually those shadiness image files that can not be ensured by the system. 1) Pretreatment After establishing image character library, it needs to make stated process for image content: filtering noise adjusting contrast of image diversification of image transform of compressed format of image transform of color space etc. The function of pretreatment is called by existing image process software via interior process. 2) Object segmentation Because existing image process software cannot reserve some character information after distilling image object (such as contour line of image object), our system has the function that it marks image object. 3) Image Character Library Image character library have mostly image information character indexing information and some assistant information. 4) Query and Process Audit artificially those shadiness image files that cannot be ensured by the system 5) Retrieval engine Use image character library to match with checked image.
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5 Conclusion The meaning of image and video information is more abundant rather than text information, thus it is more complex and difficult to analyze and understand them. Existing technique based on image content makes use of almost low-grade character of image, and there is great difference with human’s understand about a whole image on the basis of knowledge. The technique about high-level semantic understand should dip into study in the area of image processing and pattern identification all the same, which is also most key technique to improve the performance of network information audit system based on image content. There need us to take into the research hard.
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