import math
from itertools import zip_longest
from segment import segment
+from multiprocessing import Pool
# To time a run:
#
logger.addHandler(logging.FileHandler('cipher.log'))
logger.setLevel(logging.WARNING)
#logger.setLevel(logging.INFO)
+#logger.setLevel(logging.DEBUG)
english_counts = collections.defaultdict(int)
with open('count_1l.txt', 'r') as f:
for line in f:
(letter, count) = line.split("\t")
english_counts[letter] = int(count)
-normalised_english_counts = norms.normalise(english_counts)
+normalised_english_counts = norms.normalise(english_counts)
+
+english_bigram_counts = collections.defaultdict(int)
+with open('count_2l.txt', 'r') as f:
+ for line in f:
+ (bigram, count) = line.split("\t")
+ english_bigram_counts[bigram] = int(count)
+normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
with open('words.txt', 'r') as f:
keywords = [line.rstrip() for line in f]
"""Returns all n-grams of a text
>>> ngrams(sanitise('the quick brown fox'), 2)
- [('t', 'h'), ('h', 'e'), ('e', 'q'), ('q', 'u'), ('u', 'i'), ('i', 'c'), ('c', 'k'), ('k', 'b'), ('b', 'r'), ('r', 'o'), ('o', 'w'), ('w', 'n'), ('n', 'f'), ('f', 'o'), ('o', 'x')]
+ ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn', 'nf', 'fo', 'ox']
>>> ngrams(sanitise('the quick brown fox'), 4)
- [('t', 'h', 'e', 'q'), ('h', 'e', 'q', 'u'), ('e', 'q', 'u', 'i'), ('q', 'u', 'i', 'c'), ('u', 'i', 'c', 'k'), ('i', 'c', 'k', 'b'), ('c', 'k', 'b', 'r'), ('k', 'b', 'r', 'o'), ('b', 'r', 'o', 'w'), ('r', 'o', 'w', 'n'), ('o', 'w', 'n', 'f'), ('w', 'n', 'f', 'o'), ('n', 'f', 'o', 'x')]
+ ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow', 'rown', 'ownf', 'wnfo', 'nfox']
"""
- return [tuple(text[i:i+n]) for i in range(len(text)-n+1)]
+ return [text[i:i+n] for i in range(len(text)-n+1)]
def every_nth(text, n):
"""Returns n strings, each of which consists of every nth character,
return ''.join([''.join(l) for l in zip_longest(*split_text, fillvalue='')])
-def letter_frequencies(text):
+def frequencies(text):
"""Count the number of occurrences of each character in text
- >>> sorted(letter_frequencies('abcdefabc').items())
+ >>> sorted(frequencies('abcdefabc').items())
[('a', 2), ('b', 2), ('c', 2), ('d', 1), ('e', 1), ('f', 1)]
- >>> sorted(letter_frequencies('the quick brown fox jumped over the lazy dog').items())
+ >>> sorted(frequencies('the quick brown fox jumped over the lazy dog').items())
[(' ', 8), ('a', 1), ('b', 1), ('c', 1), ('d', 2), ('e', 4), ('f', 1), ('g', 1), ('h', 2), ('i', 1), ('j', 1), ('k', 1), ('l', 1), ('m', 1), ('n', 1), ('o', 4), ('p', 1), ('q', 1), ('r', 2), ('t', 2), ('u', 2), ('v', 1), ('w', 1), ('x', 1), ('y', 1), ('z', 1)]
- >>> sorted(letter_frequencies('The Quick BROWN fox jumped! over... the (9lazy) DOG').items())
+ >>> sorted(frequencies('The Quick BROWN fox jumped! over... the (9lazy) DOG').items())
[(' ', 8), ('!', 1), ('(', 1), (')', 1), ('.', 3), ('9', 1), ('B', 1), ('D', 1), ('G', 1), ('N', 1), ('O', 2), ('Q', 1), ('R', 1), ('T', 1), ('W', 1), ('a', 1), ('c', 1), ('d', 1), ('e', 4), ('f', 1), ('h', 2), ('i', 1), ('j', 1), ('k', 1), ('l', 1), ('m', 1), ('o', 2), ('p', 1), ('r', 1), ('t', 1), ('u', 2), ('v', 1), ('x', 1), ('y', 1), ('z', 1)]
- >>> sorted(letter_frequencies(sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')).items())
+ >>> sorted(frequencies(sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')).items())
[('a', 1), ('b', 1), ('c', 1), ('d', 2), ('e', 4), ('f', 1), ('g', 1), ('h', 2), ('i', 1), ('j', 1), ('k', 1), ('l', 1), ('m', 1), ('n', 1), ('o', 4), ('p', 1), ('q', 1), ('r', 2), ('t', 2), ('u', 2), ('v', 1), ('w', 1), ('x', 1), ('y', 1), ('z', 1)]
"""
counts = collections.defaultdict(int)
for c in text:
counts[c] += 1
return counts
+letter_frequencies = frequencies
def deduplicate(text):
return list(collections.OrderedDict.fromkeys(text))
return ''.join([''.join(c) for c in zip_longest(*columns, fillvalue='')])
-def caesar_break(message, metric=norms.euclidean_distance, target_frequencies=normalised_english_counts, message_frequency_scaling=norms.normalise):
+def caesar_break(message, metric=norms.euclidean_distance, target_counts=normalised_english_counts, message_frequency_scaling=norms.normalise):
"""Breaks a Caesar cipher using frequency analysis
>>> caesar_break('ibxcsyorsaqcheyklxivoexlevmrimwxsfiqevvmihrsasrxliwyrhecjsppsamrkwleppfmergefifvmhixscsymjcsyqeoixlm') # doctest: +ELLIPSIS
best_fit = float("inf")
for shift in range(26):
plaintext = caesar_decipher(sanitised_message, shift)
- frequencies = message_frequency_scaling(letter_frequencies(plaintext))
- fit = metric(target_frequencies, frequencies)
+ counts = message_frequency_scaling(letter_frequencies(plaintext))
+ fit = metric(target_counts, counts)
logger.debug('Caesar break attempt using key {0} gives fit of {1} and decrypt starting: {2}'.format(shift, fit, plaintext[:50]))
if fit < best_fit:
best_fit = fit
logger.info('Caesar break best fit: key {0} gives fit of {1} and decrypt starting: {2}'.format(best_shift, best_fit, caesar_decipher(sanitised_message, best_shift)[:50]))
return best_shift, best_fit
-def affine_break(message, metric=norms.euclidean_distance, target_frequencies=normalised_english_counts, message_frequency_scaling=norms.normalise):
+def affine_break(message, metric=norms.euclidean_distance, target_counts=normalised_english_counts, message_frequency_scaling=norms.normalise):
"""Breaks an affine cipher using frequency analysis
>>> affine_break('lmyfu bkuusd dyfaxw claol psfaom jfasd snsfg jfaoe ls omytd jlaxe mh jm bfmibj umis hfsul axubafkjamx. ls kffkxwsd jls ofgbjmwfkiu olfmxmtmwaokttg jlsx ls kffkxwsd jlsi zg tsxwjl. jlsx ls umfjsd jlsi zg hfsqysxog. ls dmmdtsd mx jls bats mh bkbsf. ls bfmctsd kfmyxd jls lyj, mztanamyu xmc jm clm cku tmmeaxw kj lai kxd clm ckuxj.') # doctest: +ELLIPSIS
for multiplier in range(1, 26, 2):
for adder in range(26):
plaintext = affine_decipher(sanitised_message, multiplier, adder, one_based)
- frequencies = message_frequency_scaling(letter_frequencies(plaintext))
- fit = metric(target_frequencies, frequencies)
+ counts = message_frequency_scaling(letter_frequencies(plaintext))
+ fit = metric(target_counts, counts)
logger.debug('Affine break attempt using key {0}x+{1} ({2}) gives fit of {3} and decrypt starting: {4}'.format(multiplier, adder, one_based, fit, plaintext[:50]))
if fit < best_fit:
best_fit = fit
return (best_multiplier, best_adder, best_one_based), best_fit
-def keyword_break(message, wordlist=keywords, metric=norms.euclidean_distance, target_frequencies=normalised_english_counts, message_frequency_scaling=norms.normalise):
+def keyword_break(message, wordlist=keywords, metric=norms.euclidean_distance, target_counts=normalised_english_counts, message_frequency_scaling=norms.normalise):
"""Breaks a keyword substitution cipher using a dictionary and frequency analysis
>>> keyword_break(keyword_encipher('this is a test message for the keyword decipherment', 'elephant', 1), wordlist=['cat', 'elephant', 'kangaroo']) # doctest: +ELLIPSIS
for wrap_alphabet in range(3):
for keyword in wordlist:
plaintext = keyword_decipher(message, keyword, wrap_alphabet)
- frequencies = message_frequency_scaling(letter_frequencies(plaintext))
- fit = metric(target_frequencies, frequencies)
+ counts = message_frequency_scaling(letter_frequencies(plaintext))
+ fit = metric(target_counts, counts)
logger.debug('Keyword break attempt using key {0} (wrap={1}) gives fit of {2} and decrypt starting: {3}'.format(keyword, wrap_alphabet, fit, sanitise(plaintext)[:50]))
if fit < best_fit:
best_fit = fit
logger.info('Keyword break best fit with key {0} (wrap={1}) gives fit of {2} and decrypt starting: {3}'.format(best_keyword, best_wrap_alphabet, best_fit, sanitise(keyword_decipher(message, best_keyword))[:50]))
return (best_keyword, best_wrap_alphabet), best_fit
+def keyword_break_mp(message, wordlist=keywords, metric=norms.euclidean_distance, target_counts=normalised_english_counts, message_frequency_scaling=norms.normalise):
+ """Breaks a keyword substitution cipher using a dictionary and frequency analysis
+
+ >>> keyword_break_mp(keyword_encipher('this is a test message for the keyword decipherment', 'elephant', 1), wordlist=['cat', 'elephant', 'kangaroo']) # doctest: +ELLIPSIS
+ (('elephant', 1), 0.41643991598441...)
+ """
+ with Pool() as pool:
+ keys = [(word, wrap) for word in wordlist for wrap in range(3)]
+ # breaks = map(lambda kw: keyword_break_one(message, kw[0], kw[1], metric, target_counts, message_frequency_scaling), keys)
+ breaks = pool.starmap(lambda k, w: keyword_break_one(message, k, w, metric, target_counts, message_frequency_scaling), keys, 1000)
+ return min(breaks, key=lambda k: k[1])
+
+def keyword_break_one(message, keyword, wrap_alphabet, metric, target_counts, message_frequency_scaling):
+ plaintext = keyword_decipher(message, keyword, wrap_alphabet)
+ counts = message_frequency_scaling(letter_frequencies(plaintext))
+ fit = metric(target_counts, counts)
+ logger.debug('Keyword break attempt using key {0} (wrap={1}) gives fit of {2} and decrypt starting: {3}'.format(keyword, wrap_alphabet, fit, sanitise(plaintext)[:50]))
+ return (keyword, wrap_alphabet), fit
+
+
+def scytale_break(message, metric=norms.euclidean_distance, target_counts=normalised_english_bigram_counts, message_frequency_scaling=norms.normalise):
+ """Breaks a Scytale cipher
+
+ >>> scytale_break('tfeulchtrtteehwahsdehneoifeayfsondmwpltmaoalhikotoeredcweatehiplwxsnhooacgorrcrcraotohsgullasenylrendaianeplscdriiotoaek') # doctest: +ELLIPSIS
+ (6, 0.83453041115025...)
+ """
+ best_key = 0
+ best_fit = float("inf")
+ for key in range(1, 20):
+ if len(message) % key == 0:
+ plaintext = scytale_decipher(message, key)
+ counts = message_frequency_scaling(frequencies(ngrams(sanitise(plaintext), 2)))
+ fit = metric(target_counts, counts)
+ logger.debug('Scytale break attempt using key {0} gives fit of {1} and decrypt starting: {2}'.format(key, fit, sanitise(plaintext)[:50]))
+ if fit < best_fit:
+ best_fit = fit
+ best_key = key
+ logger.info('Scytale break best fit with key {0} gives fit of {1} and decrypt starting: {2}'.format(best_key, best_fit, sanitise(scytale_decipher(message, best_key))[:50]))
+ return best_key, best_fit
+
if __name__ == "__main__":
import doctest
projects / cipher-tools.git / blobdiff