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The computeSpectrum() method can give you the sample s waveform or its frequency spectrum, depending on the FFTMode parameter. By default (when FFTMode is false), the method samples the audio and gives you a waveform: the wave being output to your speakers by Flash Player during the sample time. For waveforms, the x-axis or index in the array represents time, and the y-axis or value at each index represents amplitude. For example, a low humming would be visible as a long sine wave. The distance between the peaks of the waves is the tone s frequency, and the height of those waves is its amplitude. When you pass true as the FFTMode, Flash Player passes the waveform through a Fourier transform. (The FFT in FFTMode stands for Fast Fourier Transform.) This transform calculates the frequencies that contribute to the overall sound. So when you use FFTMode, instead of a waveform, you get a frequency distribution. The x-axis or index in the array represents different frequencies, from low to high, and the y-axis or value at each index represents the contribution of that frequency during the duration of the sample. For example, if you watch a frequency spectrum as a song while a well-de ned drumbeat plays, you see repeated peaks shoot up near the low end of the graph in time with the drums. The x-position of the peak is the mean frequency of the drum. Both of these kinds of audio samples use a slice of time. You can also control how wide of a slice is used by setting the sample length. This is done implicitly with the stretchFactor parameter.



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In this way, Wang has considerable control over the outputs, yet she is still able to work with the actual step functions, not approximations Another important feature of Wang s attack is that the colliding messages she finds consist of two message blocks In this way, the attack on the second block becomes, in effect, a chosen IV attack But the real magic in Wang s attack lies in the selection of the difference patterns Apparently, the input differences were selected so as to behave nicely in the later rounds More precisely, Wang takes advantage of the fact that an output difference of 2 is propagated from step to step with probability 1 in the third round and with probability l / 2 per step in a large part of the fourth round [34] In fact, this is precisely the special property.





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By default, and when the stretchFactor is 0, audio is sampled at 44.1 kHz. This means that the waveform you get back is 1/44100 of a second in duration (or the spectrum represents frequencies that occurred in that duration). When you increase the stretchFactor, it decreases the sample rate by that factor plus one, which stretches out the slice by that factor plus one. Let s say you use a stretchFactor of 1. This causes Flash Player to sample audio two times slower, at 22.05kHz, meaning that the samples are 1/22050 of a second, twice as wide. So the samples have effectively been stretched by a factor of two. You don t need to be so super-accurate with the sample rate when visualizing sound, because your ears are much more attuned to that kind of speed than your eyes. You ve probably seen both kinds of audio visualizations waveforms and frequency spectrums in action. They are compared in Figure 31-3. If you simply draw the output of calculateSpectrum() directly, you can get two passable kinds of audio visualizations, as shown in Example 31-5. But, of course, you can tie the produced data to all kinds of properties or put it through further calculations to make all kinds of interesting sound-reactive applications.

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Our first objective is to determine T ( 0 ) . Since T ( 0 )is in the Cave Table, T ( 0 )is limited to one of the 164 distinct bytes that appear in the Cave Table. For each of the 164 possible choices for T(O),we construct a 256 x 256 table A , such that Ai,j = 1 if it is possible that T ( i ) = j , and Ai,j = 0 if it is not possible that T ( i ) = j . To begin, we initialize Ai,j = 1 for all i and j . Then we make use of the restrictions inherent in the Cave Table to find impossible entries in A , that is, we find i and j for which we must have Ai,j = 0 due to the st,ructure of the Cave Table. Finally, we use the known plaintext to mark additional impossible entries in the A table. Denote the 164 distinct Cave Table bytes as

FIGURE 31-3

(22.25)

EXAMPLE 31-5

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