The Writing Style of Elie Wiesel Essay - 895 Words

The Writing Style of Elie Wiesel In the memoir Night, Elie Wiesel uses a distinct writing style to relate to his readers what emotions he experienced and how he changed while in the concentration camps of Buna, during the Holocaust. He uses techniques like irony, contrast, and an unrealistic way of describing what happens to accomplish this. By applying these techniques, Wiesel projects a tone of bitterness, confusion and grief into his story. Through his writing Wiesel gives us a window into the complete abandonment of reason he adopted and lived in during the Holocaust. Wiesel uses a black irony to emphasize the absence of normality in the concentration camps. As Eliezer marches into Auschwitz he notices a sign with the caption,†¦show more content†¦However, he uses ambiguous details to describe how the man dies, only saying, â€Å"Falling back onto the ground, his face stained with soup†¦ then he moved no more† (57). The very descriptive explanation of whe n and where the bombing occurred is not as important as the moment the man dies, yet Wiesel chooses to describe the less important event more than the other. By not telling us how the man dies he leaves us wondering and makes us conclude how and even if the prisoner dies. By making us examine the death of the prisoner more closely we are left with a deeper impression of the event. The sudden change from a peaceful day of rest to one of chaos is another way of showing the confusion Eliezer feels. The scene of the dying man resonates in our mind and shows us the horrors of the concentration camps. Wiesel also beautifully illustrates the desperation of the prisoners in Buna by telling us about a man who would risk death just to have a bit of extra soup (57). One last writing technique Wiesel employs is an almost unrealistic quality to the way he describes some events. As Eliezer travels to a new camp he is forced to stay in a shed, cramped together, one on top of another with the r est of the Jews. There he hears the sound of a violin, â€Å"in this dark shed, where theShow MoreRelatedNight And Dawn : The Revolutionizing Story Of Tragedy1663 Words   |  7 PagesNight and Dawn: The Revolutionizing Story of Tragedy Throughout the course of history, time has been kind to some, and evil to others. To Elie Wiesel, time has been a ruthless machine that only caused hardship and sorrow. Elie Wiesel had to encounter arguably the most tragic event in history, the Holocaust, which took the life of his mother, father, and siblings, in addition to 6 million other Jews. Essentially, the Holocaust stemmed from Adolf Hitler gaining power of Germany in World War II, whichRead MoreMegan Cooper . Instructor Klug. English 10B. April 21,968 Words   |  4 Pages Megan Cooper Instructor Klug English 10B April 21, 2017 Analyzing Night Style The Holocaust was the systematic and bureaucratic murder of six million Jews by the Nazi party and its collaborators. During the era of the Holocaust, German authorities targeted many groups of people because of their perceived racial inferiority including Gypsies, the disabled, and some of the Slavic peoples. Other groups were persecuted on political, ideological, and behavioral grounds. Several authorsRead MoreNight Trilogy By Elie Wiesel1075 Words   |  5 Pages14 10 June 2015 Night Trilogy Criticism Elie Wiesel’s Night Trilogy is comprised of an autobiography about Wiesel’s experience during the Holocaust and the horrific struggle he faced while in concentration camps, and two other stories depicting the rise of Israel and an accident. The acclaimed Holocaust writer is most well-known for Night due to its effect across the globe. 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Though the book is only a little over one-hundred pages, you areRead More Elie Wiesel’s Night and Corrie Ten Booms The Hiding Place Essay2856 Words   |  12 PagesElie Wiesel’s Night and Corrie Ten Booms The Hiding Place Many outsiders strive but fail to truly comprehend the haunting incident of World War II’s Holocaust. None but survivors and witnesses succeed to sense and live the timeless pain of the event which repossesses the core of human psyche. Elie Wiesel and Corrie Ten Boom are two of these survivors who, through their personal accounts, allow the reader to glimpse empathy within the soul and the heart. Elie Wiesel (1928- ), a journalist andRead MoreComparing Night And Siddhartha1111 Words   |  5 Pagesâ€Å"We go on and on about our differences. But, you know, our differences are less important than our similarities. People have a lot in common with one another, whether they see that or not† (William Hall). 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None but survivors and witnesses succeed to sense and live the timeless pain of the event which repossesses the core of human psyche. Elie Wiesel and Corrie Ten Boom are two of these survivors who, through their personal accounts, allow the reader to glimpse empathy within the soul and the heart. Elie Wiesel (1 928- ), a journalist and Professor of Humanities at Boston University, is an author of 21 books. The first of his collection, entitled Night, is a terrifying account

Data Mining for Command Line Interface -myassignmenthelp.com

Question: Discuss about theData Mining for Command Line Interface. Answer: Features of Data Mining tool Data mining tools have various features which perform various functions. The key features include graphical interface, command line interface, API, algorithms, In-memory, interactive dashboard, multiple file support, and data management methods. Each data mining tool has an interface which allows users to interact with the tool. Some tools have a graphical user interface (GUI) while others have both GUI and command line interfaces. GUI is aimed at allowing users to complete data mining projects without the need of programming languages (Mikut, 2011). GUI is relatively easy to use for most people especially non-programmers. This is because command line interface (CLI) require technical knowledge in various programming languages such as python, R, Java, etc. Data mining tools with CLI allow users to access all features and are useful for scripting large data mining jobs. Most data mining tools have APIs which are key in data mining (Han, 2011). These APIs are used to perform varying functions. For example, a particular API can be used to mine trends from input data. Webhost.io is a data mining API that allows users to find structured web data that an be leveraged to scale big data operations. Other functions of the APIs include extracting data from the web, grouping sentences or short texts, retrieving data from wiki data store, encrypt data, etc. Data mining tools such as Weka and Rapid Miner have an API which can be integrated into custom applications. Data mining is dependent on algorithms which are designed to analyze specific aspects in a dataset. Data mining tools have a set of algorithms which are used independently or in combination to analyze the data. Selection algorithms are the most common in data mining tools. They are classified as wrapper, filter, and embedded methods. Filter methods rely on a measure to assign a score to each feature. Some of the filter methods include information gain, and Chi-squared test. Wrapper methods view the selection process as a search problem which involves different combinations that have to be compared. The methods consider a predictive model which assigns a score and apply a methodical search process. Recursive feature elimination algorithm is an example of a wrapper method. Also, embedded method is one of the features in data mining tools which are in improving the accuracy of models. It is common for data mining tools to have an in-memory database which is a system that uses main memory for data storage. Main memory is much faster than disk databases as access to disk tends to be slow. This feature is critical in enhancing the processing speed of the tool when mining data. Main memory incorporates simple internal optimization algorithms and has few CPU instructions which enhances the performance of the data mining tool. These tools also integrate an interactive dashboard that includes various options that users can leverage to view the results of the data mining process. The dashboard is aimed at making the tools easy to use for many people (Romero, 2008). With a dashboard, new users can easily apply data filters and algorithms to analyze the datasets available. It also allows users to create charts and graphs from the data. Since data mining tools handle various kinds of data from different sources, they support multiple file formats. Some tools may handle specific data format, but most support a lot of formats. Some of the formats supported including CSV, XML, HTML/A, TIFF, GeoTIFF, MP3, MOV, among others. Multiple file support is an important feature that is considered when purchasing data mining tool. Additionally, these tools have various data management methods aimed at enhancing the data mining process. Some of the methods are data preparation and data filtering. Before processing the dataset obtained, the data has to be filtered to avoid skewed results that may not represent the reality. How data mining realize the value of data warehouse Data warehouse plays an instrumental role in integrating data from different databases. The objective of the data warehouse is not to store data but help firms to make informed decisions based on the insight gained from the data. It supports this goal by offering an architecture for organizing and assessing data from various sources. In data warehouses, data may be stored in flat files, database tables, or spreadsheets. To realize the value of a data warehouse, it is critical to obtain knowledge from the information stored. However, due to the amount and complexity of data, it is challenging for data analysts to determine trends and relationships using simple query tools. Data mining is an effective way of extracting knowledge such as trends and patterns from the data. Data mining process involves analyzing data and generating useful information. It relies on complex data analysis tools to identify patterns and relationships in datasets stored within the data warehouse. These tools are more advanced than querying tools as they use complicated algorithms to analyze the datasets (Van der Aalst, 2011). With data mining, firms can leverage their large datasets to identify patterns that may have business implications. Many businesses apply data mining to gain business intelligence that is vital in aligning with market trends and competing with rivals. Data mining enables businesses with warehouses to identify patterns that can be used to predict trends. Data mining tools include predictive models which assist in predicting future trends based on the patterns observed in the datasets. For example, a fashion company that sells fashion products to its customers and has a data warehouse can leverage data mining tools to predict future trends in the fashion industry. Data on customer purchasing behavior as well as customer growth can be vital in predicting business growth expected by the company (Ngai, 2009). For firms that work in the marketing industry, it is essential to understand customer behavior and habits. Such firms have data warehouses that hold customer data and their purchasing history. With data mining systems, the firms can analyze customer data and determine customer profiles. Results from such a process are helpful in monitoring customer habits. The firms can gain value from the results by leveraging them to build customer-oriented marketing campaigns. Knowledge can be vital when making decisions. While data warehouse contains vast amounts of data, firms cannot benefit from it unless they obtain knowledge. Data mining helps to identify important patterns and relationships which can be incorporated into business applications. Through data mining, firm managers can have access to crucial insight that can help them to make precise business decisions. Firms that collect information from their customer and operations have a competitive advantage over their rivals. Such information can be mined to acquire knowledge about market changes, customer, and preferences. Data mining is key in supporting market-based analysis on the data available in the data warehouses. The process involves information that is gathered on the basis of market information from various sources. With data mining tools, firms can analyze the market and identify key trends that should be considered in business planning to maintain the competitiveness of the firm in the market. Additionally, data mining allows banks to gain value and protect their operations. Marketing analysis which is enabled by data mining process helps bank firms to find fraud. Banks can identify customers involved in fraud and close their accounts to protect their operations. References Han, J., Pei, J., Kamber, M. (2011).Data mining: concepts and techniques. Elsevier. Mikut, R., Reischl, M. (2011). Data mining tools.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,1(5), 431-443. Ngai, E. W., Xiu, L., Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification.Expert systems with applications,36(2), 2592-2602. Romero, C., Ventura, S., Garca, E. (2008). Data mining in course management systems: Moodle case study and tutorial.Computers Education,51(1), 368-384. Van der Aalst, W. M. (2011). Data Mining. InProcess Mining(pp. 59-91). Springer Berlin Heidelberg.

Influence of television in our society Essay Example

Influence of television in our society Essay Media is very important in today’s world, especially television. We now live in the Information Age and it is through media that information is transmitted. In olden days, media was not developed properly and so transmitting information was difficult. Starting from newspapers and magazines, technology had improved steadily to give us radio and television. More recently it has given us the Internet. Television plays an important role by giving us news and analysis about important events across the world. Television has both good influences and bad influences on society. The following essay will talk about these in detail. There is no doubt that Television has many good influences. Firstly, Television is an audio/visual medium making it easy for the audience to follow programs. It uses graphics, sound recording and film technology to bring lively and interesting material to the enjoyment of the audience. Many people when they come back from office or school switch on the Television and relax themselves. More importantly, it also provides news bulletins and science programs for gaining knowledge. For school-going children it offers sport programming like live baseball telecast, etc. All this are very useful and perform an important social role. In fact, Television has become such an important part of everyday life that we cannot imagine how life will be without Television. (Bignell, 2004) We will write a custom essay sample on Influence of television in our society specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Influence of television in our society specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Influence of television in our society specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Television has been used for the purpose of education as well. For example, class-rooms can show students interesting movies that are part of the curriculum. Class-room can also show recorded lectures and science documentaries. With respect to society, Television brings awareness about global issues so that all of us can act together. One example of this is global warming, about which information is provided in Television. With this we can act together and save the planet. Television also has plenty of advertisements. While some ads can be boring, some others provide information about products, which we can use when we go shopping. (Noll, 2011) Television not only performs a social role but also a family role. It brings all members of family together during evenings. In fact, it has become part of family routine at dinner time. Television gives news about weather conditions. We can take precautions against rain and storm by watching weather news. Also, Television is used these days in closed-circuit cameras for security reasons. On the negative side too, there are many points. First, watching Television for long time is bad for the eyes. Television can distract students from focussing on studies. It can make young children lazy and keep them away from books and sports. Parents are especially worried that Television has bad effect on society as it shows violence and crime. Young people can get the wrong idea about life watching such programs. For example, a young person watching use of guns on Television might later kill someone with a gun in real life. So it is very dangerous in cases like this. It also gives wrong idea about how to treat women, etc. In other words, Television can show women in stereotype fashion. Hence, not everything about Television is good. (Freedman, 2002) Television can cause problems in society by showing racial discrimination, etc. For example, if black people are not shown in programs, one gets the impression that they are not important people. Television news can also be flawed. For example, we cannot believe everything the news anchor says as truth. Sometimes they give inaccurate information to deceive the viewer. So one has to be careful about the truthfulness of news. Also, the information given in advertisements can be misleading. Many companies are just trying to make quick money and will show any false claims to sell products. So we have to be careful while watching Television. (Bignell, 2004) Finally, Television has many limitations which make it not useful for in-depth analysis. That is, Television can never replace the importance of books and libraries as sources of detailed information. If society relies on Television as the sole medium of instruction and knowledge than we are all doomed. (www.buzzle.com, 2011) Hence, in conclusion, Television, along with other media, is neither wholly good to society or wholly bad. There are good and bad aspects to it and positive and negative influences on society. It is up to us to be choosy in watching Television. We do not want to be cheated by false advertisements and false news. We also don’t want to get wrong impression about women and gun usage. We should use Television for good things like developing knowledge and improving language skills, etc. So, we need to think about both sides of the argument and be selective in watching Television. It is apt to conclude by saying that books have more beneficial effect on society than Television. References: Paul Noll, Television Plays a Positive Role in Society, Arguments in Favour, retrieved from on 31st January, 2011. Negative Effects of Television, retrieved from on 31st January, 2011. Freedman, J (2002). Media violence and its effect on aggression.: Assessing the scientific evidence. University of Toronto Press. Bignell, Jonathan. An Introduction to Television Studies (New York: Routledge, 2004)

All In One Social Media App What Makes CoSchedule the Best

All In One Social Media App What Makes the Best Managing your social media is†¦ well, it isn’t easy. At first it seems like it should take way less  time than a blog or email†¦ But it actually is a MAJOR time suck. First, you have to plan out all your posts Which includes spending time trying to pinpoint the BEST time to publish  your content based on your audience while still making sure you don’t have any gaps in your schedule. Next, you spend a large amount of time creating + curating your social media content†¦ only for it to drop into social media oblivion (a.k.a the very bottom of a newsfeed) just hours or even minutes  after it’s posted. *insert tiny sobs here* ^^#reallife This doesn’t even take into consideration all the social networks (and every associated username and password) you have to manage in that packed spreadsheet of yours. And to make matters worse†¦ After you’ve already spent all that time creating and posting your content†¦ taking any extra time (if you have any) to measure the effectiveness + reach of your social media feels SUPER tedious. And very un-fun. Because your marketing plan is more than just social media.

Market structure Essay Example | Topics and Well Written Essays - 1750 words

Market structure - Essay Example According to Baumol and Blinder (2011 p. 200), such a market must satisfy four conditions. First, the market has many small firms and customers such that no participants are large enough to have market power to affect the price of a product. If one producer reduces the price, there would be no effect on the market since the producer is negligible compared to the whole market. This condition rules out the possibilities for collusion or trade associations; each firm acts independently (Tucker, 2010). Secondly, all the suppliers sell a homogeneous product; there are no close substitutes (McEachern, 2011). As such, the consumers buy products from any seller since the products are the same thus competition is very powerful. The demand curve is perfectly elastic hence if a seller increases the price of the product, customers shift to buy competitors products. The firms have no choice but to meet and not exceed the price charged by others hence are â€Å"price takers† (Baumol & Blind er, 2011 p. 201). Thirdly, there are no barriers to entry or exit in the market. Barriers to entry may be in form of legal, technical or cost advantage but in a perfectly competitive market, any seller willing to enter the industry can do so to take advantage of economic profits and provide an identical product (p. 200). The new entrant is at the same level with the old firms; there are no advantages for existing firms so the new firm can compete effectively. Lastly, the infinite buyers and sellers have perfect information regarding the price and quality of products in the market. As a result, there is no need for advertising as it would have no effect; the customers know where to buy their products and besides, all products are identical and the price is determined by the market. According to Landsburg (2011), in a perfectly competitive market there are no transaction costs and perfect factor mobility. This enables the market to adjust accordingly in case of changing market conditi ons. Q2: Price and Output Decisions of a Perfectly Competitive Market As noted above, there are infinite buyers and sellers in the market such that none has an effect on price. The price in such a market is determined by forces of supply and demand hence the sellers are â€Å"price takers†. Sexton (2012) argues that since the market price is given, the only decision that firms have to make is determining the level of output that would maximize profit. The question firms should ask themselves as asserted by McEachern (2011 p. 176) is â€Å"how much should I produce?† He notes that firms aim at producing a quantity at which total revenue is higher than total cost by the greatest amount. The profit maximizing output in a perfectly competitive market occurs where marginal revenue (MR) is equal to marginal cost (MC); MR=MC therefore the firms are seen to allocate resources efficiently. A perfectly competitive firm has a horizontal demand curve thus it can sell as much quant ity as it wants at the given market price. Whether the firm increases its output or not, the price remains the same as there are many sellers. It also does not have to reduce the price so as to attract demand as it would lead to loss of revenue for the firm (Baumol & Blinder, 2011). Since total revenue is the output multiplied by the price, the average revenue is the same as price. The firm is also a price taker hence the marginal revenue is equal to

Explain why Perfectly Competitive Industries are Considered to be Essay

Explain why Perfectly Competitive Industries are Considered to be Efficient in the Short and Long-Run - Essay Example There are pros and cons to both yet all reside on one opinion that the key to efficiency is competition. In order to present a concrete conclusion, we have to get into a profound discussion so as to compare pros with cons of perfectly competitive market and also discuss the variance it has with monopoly structure. Perfect Competition and Efficiency: According to Adam Smith perfectly competitive market works under â€Å"invisible hand† in which each individual in society seeks out for the personal interest. However, in order to preach it, he/she has to trade off his belongings with the individual who is willing to get benefited from it. This ultimately leads to benefit of society intentionally or unintentionally. Theoretically; there are many buyers and sellers, identical products, no barriers to entry as well as exit (Thomas E. Woods, 2011). Buyers and sellers both have the perfect information and hence they are the â€Å"price takers† which results in a perfectly elast ic demand curve. This means that if a firm wants to maximize its profit it should sell its product at market price. This means that efficiency is required to keep the cost down and increase the net profit margin. Efficiency is realized when all opportunities to make someone better off without making anyone worse off are exhausted. It is also called ‘Pareto Efficiency’ in most of the global conceptions (Books Llc, 2010). Under ideal conditions in a perfectly competitive market or any other market which is functioning well, the market equilibrium maximizes the difference between the benefits society gets from the good and services and what it costs society to produce. A perfectly competitive market would always focus on the maximum net social benefit. Benefit is not only considered by the monetary return achieved from investments but also the implicit gain realized by the society as a whole. An efficient allotment of resources is accomplished if increment in societyâ€⠄¢s overall level of satisfaction by more of one good and less of another good is not possible. This is why competition is preferred as it mostly leads to favorable outcomes. Competition urges players to perform better than their rival which ultimately leads to better market mechanism. Such efficiency is realized by entities if the price of a good is equal to the marginal cost of the product. An elaboration of the above mechanism is as follows: We know from the above discussion that market supply shows the marginal cost of society of producing the good or service. Moreover, the demand curve is the marginal benefit to society from consuming the good or service. Therefore, the net social benefit would be maximized if the marginal social cost is equal to the marginal social benefit (Tucker, 2010). Considering the cost allocated to society, the market supply is the horizontal sum of each firm’s MC curve or in other words it shows what it costs to produce one additional unit of go od. Economist says that if all the costs are digested by the firms, then supply equals the marginal social cost (Lambert M. Surhone, 2010). On the other hand, economists’ measure benefits in terms of the willingness of consumers to pay therefore, the market demand would be a representation of the total sum of willingness to pay for a unit of good at each level of consumption. The market demand mechanism focuses on maximization of social benefit illustrated below: All consumers whose WTP (willingness to pay) exceeds P will buy a good (or more

Pixel and Edge Based Lluminant Color Estimation

Pixel and Edge Based Lluminant Color Estimation Pixel and Edge Based Lluminant Color Estimation for Image Forgery Detection Shahana N youseph  and Dr.Rajesh Cherian Roy ABSTRACT Digital images are one of the powerful tools for communication. So Image security is a key issue when use digital images. With the development of powerful photo-editing software, such as Adobe Photoshop Light room 4, Apple Aperture 3, Corel PaintShop Pro X5, GIMP 2.8, photo manipulation is becoming more common. In this paper mainly detecting forged peoples in images. The main idea for the detection is, different images are captured under different illuminant condition, when combining these image fragments from different images, it is difficult to match the illumination conditions. This inconsistency of illumination leads to forgery detection. The main contribution of this method of forgery detection is how illuminant color can be used as a clue for forgery detection. The proposed method will be able to detect forgery using Linear SVM classification, with 70%-75% of accuracy. Keywords Pixel based illuminant color estimation; Edge based illuminant color estimation, I. INTRODUCTION Every day, millions of digital documents are produced by a variety of devices. They are distributed by newspapers, magazines, websites and television etc. In all these information channels, images are a powerful way for communication. It is not difficult to use computer graphics and image processing techniques to manipulate or to forge images. Video footage, scanned images, as well as digital and analogue images can be the target for manipulations. From a forensics perspective, several changes in a photograph are widely acceptable for improve the quality of images, e. g. to enhance the contrast, denoise an image, or highlight important regions etc.Forensics Science is a department for criminal investigation in distinct areas such as digital forensics, analogue forensics, multimedia forensics, network forensics etc.Image Forgery is the process of creating doctored/fake images, with the development of advanced image processing software’s such as Adobe Photoshop Light room 4, App le Aperture 3, Corel PaintShop Pro X5, GIMP 2.8 etc forgeries in images is easy process. Image Forgery detection is Active and Passive. Digital watermarking is an example of active. The passive image forgery detection is a blind approach, which means it does not have any prior knowledge of input image. There are various methods used for the checking of authenticity of images. In this paper the method used is based on illumination. When light fall on an object color of the object is reflected, depends on illuminant color/light color. Objects having different color in different illumination condition. So when we forge an image or making composite of various images it is very difficult to maintain the consistency of illumination. Illumination is one of the criteria for forgery detection. Some other criteria’s are used for passive image forgery detection such as, JPEG compression properties, Projective geometry, Chromatic aberration, Color filter array (CFA) and inter pixel corre lation etc. Literature Survey Table1. Illuminant Color Based methods Proposed Method First step is cropping face of the input image. This proposed method is mainly detecting forged peoples in an image. The estimation of the illuminant color is error-prone and it is affected by the materials in the scene, the illuminant color estimates on objects of similar material exhibit a lower relative error.Thus,the illuminant color detection to skin, mainly to faces. Pigmentation is the most obvious difference in skin characteristics. Second step is illuminant colour estimation, explained in next section. Fourth step is generation of illuminant map. Image is segmented with graph cut segmentation. Illuminant color is estimated using static methods on each segmented output with same index number. Based on the estimated illuminant color, apply it for the segments with same index number. The resulting output will be RGB components. This coloured representation of image with R G B components is termed as Illuminant Map. Fifth step is shape and colour feature extraction. For shape fe ature HOG Edge feature is used. An edge of illuminant map is extracted using various edge detection methods. Histogram of Oriented Gradients of edge points. For colour feature extraction. Colour Moments feature is used. Moments with first and second moments are extracted. Last step is SVM classification. Classify the illumination for each pair of faces in an image as either consistent or inconsistent. Assuming all selected faces are illuminated by the same light source, Train the SVM with two class with one class is for forged image and other for original image.When testing operation performed based on the test feature value image is classify either forged or original. ILLUMINANT COLOR ESTIMATION Pixel Based Illuminant Color Estimation Pixel values of the entire are taken for illuminant color estimation. In this methods focussed on low level features. Such as Grey World, Max-RGB, Shades of grey. Simple and less complex calculation is used for the estimation, with the help of some static variables. So it is also known as static illuminant color estimation. Grey World Hypothesis: In Grey World, Illuminant color is estimated from Average Pixel values of images. Under a neutral light source or white light source, Average reflectance of the entire image is achromatic (Having no colors), if any deviation from this condition is due to color of illumination. This average reflected color will be the color of the light source. Max-RGB Hypothesis: In Max-RGB, illuminant color estimated from maximum response of Red Green Blue (RGB) channel. Maximum response is obtained from perfect reflectance. A surface having perfect reflectance property will respond (reflect) for the full range of light colors it captures, when light incident on it. Then this reflected color is actually the color of light source. Shades of Grey: Grey world and the max-RGB illuminant color estimation in terms of Minkowski norm, is called shades of gray. , If p=1 Grey World Estimation If p=∞ Max-RGB Estimation If p=6 Shades of Grey Estimation Edge Based Illuminant Color Estimation Edge based illuminant color estimation is use low or higher order derivatives. In this methods edges and colors towards illuminant direction. In order to accurately estimate color of light source is use the pixel and edge points that coincide the illuminant direction. Highlights produce such types of points. In edge based estimation contains Grey edge and Weighted Grey edge estimation are used. In Weighted grey edge methods, using some weighting fuction to the edges. For that classifying the edges based on the photometric properties, material edges (e.g. edges between objects and object-background edges), shadow/shading edges (e.g. edges caused by the shape or position of an object with respect to the light source) and specular edges (i.e. highlights).These edges perform better influence on illuminant estimation. In Weighted Grey edge methods computing weighted average of edge points. The iterative weighting scheme is proposed, and by assigning this weighting scheme in to the grey ed ge method, the color of the light source is estimated. Edge based illuminant color estimation mainly contain, †¢ First Order Grey Edge †¢ Second Order Grey Edge †¢ Weighted Grey Edge First Order Grey Edge: The pth Minkowski norm of the first derivative of the reflectance in a scene is estimated. Computed by, Second Order Grey Edge: The pth Minkowski norm of the second derivative of the reflectance in a scene is estimated. Weighted Grey-Edge: Weighted Grey-Edge algorithm is computed by assigning a weighting function to the illuminant estimate. This weighting function is estimated by classifying edges based on the photometric properties and an iterative edge weighting scheme is generated. †¢ Derivative order x: the assumption that the average of the illuminants is achromatic can be extended to the absolute value of the sum of the derivatives of the image. †¢ Minkowski norm p: instead of simply adding intensities or derivatives, respectively, greater robustness can be achieved by computing the p-th Minkowski norm of these values. †¢ Gaussian smoothing ÏÆ': to reduce image noise, one can smooth the image prior to processing with a Gaussian kernel of standard deviation. Specular Edge Weighting scheme: Specular weighting scheme is the ratio of the energy in the specular variant versus the total amount of derivative energy. This ratio translates to the specular edge weighting scheme given by: , where , Results To check the accuracy of forgery detection using SVM classifier with SVM is trained with 50 forged and 50 original images and SVM is tested using total of 50 images where 25 are original and 25 are composite images downloaded from different websites in the Internet.SVM is trained several times for several testing process. First set of forgery detection testing is done with various illuminant estimation methods such as Grey World, MAX-RGB, Shades Of Grey and Grey Edge First and Second Order and weighted grey edge with shape feature and color feature extraction separately. In shape feature called HOG Edge use various edge detection methods such as Canny, Roberts, Prewitt, and Sobel for the comparative study. And finally the combination of color moment and HOG Edge is tested for forgery detection. Confusion matrix is generated accuracy is calculated. Accuracy=TP+TN/(TP+TN+FP+FN) Where, True Positive (TP) input-Forged, Output-Forged True Negative (TN)- input-not forged ,output-not forged False Positive (FP) -input-forged, output-not forged False Negative (FN)-input-not forged, output-forged . Table 2. Estimated Accuracy Of fogery detection with Various Illuminant Color Estimation Methods From the above result, when using all static illuminant color estimation method for forgery detection Weighted grey edge peform well when compare with other methods. Feature extraction used is HOG Edge and Color moments features for shape and color feature extraction. If use one feature extraction method only get 50%-64% of accuracy. If use combined HOG Edge and Color Moments features accuracy is improved to 66%-74%. Conclusions Presented a new method for detecting forged images of people using the illuminant color Estimation. Estimate the illuminant color using Pixel and Edge based Illuminant estimation method, and generation of illuminant map. Canny edge detector are used to obtain edges of illuminant map for the extraction of shape features using HOG Edge descriptor, which is used to get Histogram of oriented Gradients of edge points. For color feature extraction use color moments features. These two features are tested separately with different illuminant estimation method for the comparative study. Combination of these two features is also used for forgery detection for the comparative study.From the result it is clear that combined HOG Edge and color features get more accuracy than method used shape and color features separately.Accuracy is Estimated using SVM Classifier.The Combined feature extraction with Weighted grey edge testing process get 74% of accuracy. The proposed method requires only a mini mum amount of human interaction and provides a crisp statement on the authenticity of the image. Additionally, it is a significant advancement in the exploitation of illuminant color as a forensic cue. Prior color-based work either assumes complex user interaction or imposes very limiting assumptions. FUTURE WORK The accuracy of the classification can be improved by using adding more content based features. Use of training based illuminant color estimation also improves accuracy. References [1] Hany Farid ,à ¢Ã¢â€š ¬-Image Forgery Detection [A survey]à ¢Ã¢â€š ¬-, IEEE signal processing magazine March- 2009 [2] C. Riess and E. Angelopoulou, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Scene illumination as an indicator of image manipulation,à ¢Ã¢â€š ¬- Inf. Hiding, vol. 6387, pp. 66–80, 2010. [3] Gajanan K. Birajdar ,Vijay H. Mankar ,à ¢Ã¢â€š ¬-Digital image forgery detection using passiv techniques: A surveyà ¢Ã¢â€š ¬-Elsevier 2013 . [4] J. van de Weijer, T. Gevers, and A. Gijsenij, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Edge-based color constancy,à ¢Ã¢â€š ¬- IEEE Trans. Image Process., vol. 16, no. 9, pp. 2207–2214, Sep. 2007. [5] M. Johnson and H. Farid, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Exposing digital forgeries by detecting inconsistencies in lighting,à ¢Ã¢â€š ¬- in Proc. ACM Workshop on Multimedia and Security, New York, NY, USA, 2005, pp. 1–10. [6] Yingda Lv Xuanjing Shen Haipeng Chen, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢An improved image blind identification based on inconsistency in light source directionà ¢Ã¢â€š ¬- in Springer Science+Business Media, LLC 2010 [7] M. Johnson and H. Farid, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Exposing digital forgeries in complex lighting environments,à ¢Ã¢â€š ¬- IEEE Trans. Inf. Forensics Security, vol. 3, no. 2, pp. 450–461, Jun. 2007 [8] M. Johnson and H. Farid, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Exposing digital forgeries through specular highlights on the eye,à ¢Ã¢â€š ¬- in Proc. Int. Workshop on Inform. Hiding, 2007, pp. 311–325. [9] E. Kee and H. Farid, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Exposing digital forgeries from 3-D lighting environments,à ¢Ã¢â€š ¬- in Proc. IEEE Int. Workshop on Inform. Forensics and Security (WIFS), Dec. 2010, pp. 1–6. [10] W. Fan, K. Wang, F. Cayre, and Z. Xiong, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢3D lighting-based image forgery detection using shape-from-shading,à ¢Ã¢â€š ¬- in Proc. Eur. Signal Processing Conf. (EUSIPCO), Aug. 2012, pp. 1777– 1781. [11] E. Kee and H. Farid, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Exposing digital forgeries from 3-D lighting environments,à ¢Ã¢â€š ¬- in Proc. IEEE Int. Workshop on Inform. Forensics and Security (WIFS), Dec. 2010, pp. 1–6. [12] S. Gholap and P. K. Bora, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Illuminant colour based image forensics,à ¢Ã¢â€š ¬- in Proc. IEEE Region 10 Conf., 2008, pp. 1–5. [13] X.Wu and Z. Fang, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Image splicing detection using illuminant color inconsistency,à ¢Ã¢â€š ¬- in Proc. IEEE Int. Conf. Multimedia Inform. Networking and Security, Nov. 2011, pp. 600– [14] P. Saboia, T. Carvalho, and A. Rocha, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Eye specular highlights telltales for digital forensics: A machine learning approach,à ¢Ã¢â€š ¬- in Proc. IEEE Int. Conf. Image Processing (ICIP), 2011, pp. 1937– 1940. [15] C. Riess and E. Angelopoulou, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Physics-based illuminant color estimation as an image semantics clue,à ¢Ã¢â€š ¬- in Proc. IEEE Int. Conf. Image Processing, Nov. 2009, pp. 689–692. [12] S. Gholap and P. K. Bora, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Illuminant colour based image forensics,à ¢Ã¢â€š ¬- in Proc. IEEE Region 10 Conf., 2008, pp. 1–5. [16] K. Barnard, V. Cardei, and B. Funt, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢A comparison of computational color constancy algorithms–Part I: Methodology and Experiments With Synthesized Data,à ¢Ã¢â€š ¬- IEEE Trans. Image Process., vol. 11, no. 9, pp. 972–983, Sep. 2002. [17] K. Barnard, L. Martin, A. Coath, and B. Funt, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢A comparison of computational color constancy algorithms – Part II: Experiments With Image Data,à ¢Ã¢â€š ¬- IEEE Trans. Image Process., vol. 11, no. 9, pp. 985–996, Sep. 2002. [18] A. Gijsenij, T. Gevers, and J. van deWeijer, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Computational color constancy: Survey and experiments,à ¢Ã¢â€š ¬- IEEE Trans. Image Process., vol. 20, no. 9, pp. 2475–2489, Sep [19] P. F. Felzenszwalb and D. P. Huttenlocher, à ¢Ã¢â€š ¬Ã¢â‚¬ ¢Efficient graph-based image segmentation,à ¢Ã¢â€š ¬- Int. J. Comput. Vis., vol. 59, no. 2, pp. 167–181, 2004