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At any rate, to be effective, a dictionary attack requires two things: a dataset of sufficiently long Passwords to start with and an effective search algorithm. The former has gotten rather easier thanks to the proliferation of web-based tools like LastPass, 1Password, RoboForm and others that make it easy to collect lists of Passwords for a variety of web services.
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A hash function produces a digest or fingerprint for a given input. If you have two different files, say a and b, and you hash each of them, you get a fingerprint, F h(a) and F h(b). If you then have a list of Passwords, one for each file, you can generate a dictionary, which is a list of all possible key values for the hashed files.
Now, in this case, an attacker would have to examine every file to find a match, and in any reasonably large dictionary, this is infeasible. On the other hand, if the attacker has a list of words for every conceivable letter string that can be found in a dictionary, then every input can be hashed and compared to find a collision.
Let's look at the situation more specifically: Suppose we had a small dictionary that contained all of the words we can find in a password file with a length of 30,000 characters, which is far more than we would need for an actual dictionary attack.
To ensure that digital signatures are indeed unique, the algorithms are designed to ensure the smallest possible probability of two documents having the same signature. There are also other advantages: signatures usually create smaller files, and because they are binary in nature, they can be transferred faster than text documents. They can also be verified very quickly using software or hardware.