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Research Tools The following is a brief overview of some of the tools that have come out of research labs and universities in the last few years:2 ARCHER is a static analysis tool for checking array bounds (The name stands for ARray CHeckER) ARCHER uses a custom-built solver to perform a path-sensitive interprocedural analysis of C programs It has been used to nd more than a dozen security problems in Linux, and it has found hundreds of array bounds errors in OpenBSD, Sendmail, and PostgreSQL [Xie et al, 2003] The tool BOON applies integer range analysis to determine whether a C program is capable of indexing an array outside its bounds [Wagner et al, 2000] Although it is capable of finding many errors that lexical analysis tools would miss, the checker is still imprecise: It ignores statement order, it can t model interprocedural dependencies, and it ignores pointer aliasing Inspired by Perl s taint mode, CQual uses type quali ers to perform a taint analysis to detect format string vulnerabilities in C programs [Foster et al, 2002] CQual requires a programmer to annotate a small number of variables as either tainted or untainted, and then uses typeinference rules (along with preannotated system libraries) to propagate the quali ers After the quali ers have been propagated, the system can detect format string vulnerabilities by type checking The Eau Claire tool uses a theorem prover to create a general speci cationchecking framework for C programs [Chess, 2002] It can be used to nd such common security problems as buffer over ows, le access race conditions, and format string bugs he system checks the use of standard library functions using prewritten speci cations Developers can also use speci cations to ensure that function implementations behave as expected Eau Claire is built on the same philosophical foundation as the extended static checking tool ESC/Java2 [Flanagan et al, 2002] LAPSE, short for Lightweight Analysis for Program Security in Eclipse, is an Eclipse plug-in targeted at detecting security vulnerabilities in J2EE applications It performs taint propagation to connect sources of Web input with potentially sensitive operations It detects vulnerabilities such as SQL injection, cross-site scripting, cookie poisoning, and parameter manipulation [Livshits and Lam, 2005].

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In many cases, after the reverse filtering process is complete the data is ready to display If an application has the required information, the decoder can color correct the pixel data If the PNG file contains a cHRM chunk, the decoder can convert the pixel data to CIE 1931 format to get he exact colors shown on the source display, then correct the data for the destination display If the file contains a gAMA chunk, the data can be Gamma corrected for the output display 16- to 8-bit Conversion Most current computer systems only support bit depths of up to 8 Unless you are writing a decoder for a specialized system that supports greater bit depths, you are going to have to convert 16-bit data values to 8 bits The technically correct method to convert pixel values from one bit depth to another is Equation 141 Bit Depth Conversion New Value = Old Value The easiest method to convert from 16 to 8 bits is to discard the low-order byte of each 16-bit color value after applying color correction (if applicable) The results are virtually indistinguishable from that of Equation 141 Either of these two methods could create large, solid blocks of colors that could look odd, especially in photographs In some situations you may wish to apply dithering during the color conversion Transparency If the color type is RGB with Alpha or grayscale with Alpha, or if the PNG file contains a tRNS chunk, transparency can be applied if desired If the image is being drawn on a background and the decoder has access to the background's color data, the image in the PNG file can be combined with the background pixels using the process described in the previous chapter Another possibility is to combine the image with a solid background that is specified by the application or from a bKGD chunk.

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Color Quantization The JPEG and PNG formats covered in this book can contain image data that uses 24 bits or more per pixel How can you display a 24-bit JPEG image on an output device that supports only 8 bits per pixel Likewise, how can you convert a JPEG file to the GIF format, which supports maximum of 8 bits per pixel The answer is a process known as color quantization, a fancy name for the process of reducing the number of colors used in an image The topic of color quantization falls outside the scope of this book; therefore, we are only going to cover enough to allow you to display images if you are using a display that does not support 24-bit images and to convert to such formats The method we are going to use is called median cut quantization In it we create a three-dimensional count of the number of times each pixel color value is used Such a counting would look something like that shown in Algorithm 11 Now we have a cubic region containing all the color frequencies The next step, shown in Algorithm 12, is to divide the cube into two rectangular regions in such a manner that each part has half the frequency values This fragment divides the frequencies in half along the blue axis Figure 111 shows the result of the first application of the process in Algorithm 12 We apply the process recursively, dividing the rectangular regions in half using a different color axis until we have 256 boxes with roughly the same total frequency Figures 112 and 113 show sample divisions of the RGB colorspace along the other axes Keep in mind that this is a rough outline If 4-byte integers were used to store frequency values, the FREQUENCIES array defined in Algorithm 11 would.

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