import norms
import logging
from itertools import zip_longest, cycle, permutations
-from segment import segment, Pwords
+from segment import segment
from multiprocessing import Pool
from math import log10
import matplotlib.pyplot as plt
from cipher import *
+from language_models import *
# To time a run:
#
# timeit.timeit('keyword_break(c5a)', setup='gc.enable() ; from __main__ import c5a ; from cipher import keyword_break', number=1)
# timeit.repeat('keyword_break_mp(c5a, chunksize=500)', setup='gc.enable() ; from __main__ import c5a ; from cipher import keyword_break_mp', repeat=5, number=1)
-
-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)
-
-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)
-
-english_trigram_counts = collections.defaultdict(int)
-with open('count_3l.txt', 'r') as f:
- for line in f:
- (trigram, count) = line.split("\t")
- english_trigram_counts[trigram] = int(count)
-normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
-
-
-with open('words.txt', 'r') as f:
- keywords = [line.rstrip() for line in f]
-
transpositions = collections.defaultdict(list)
for word in keywords:
transpositions[transpositions_of(word)] += [word]
# counts[c] += 1
#return counts
return collections.Counter(c for c in text)
-letter_frequencies = frequencies
-def bigram_likelihood(bigram, bf, lf):
- return bf[bigram] / (lf[bigram[0]] * lf[bigram[1]])
+def frequency_compare(text, target_frequency, frequency_scaling, metric):
+ counts = frequency_scaling(frequencies(text))
+ return -1 * metric(target_frequency, counts)
+def euclidean_compare(text):
+ return frequency_compare(text, norms.euclidean_scale(english_counts),
+ norms.euclidean_scale, norms.euclidean_distance)
-def caesar_break(message,
- metric=norms.euclidean_distance,
- target_counts=normalised_english_counts,
- message_frequency_scaling=norms.normalise):
+
+def caesar_break(message, fitness=Pletters):
"""Breaks a Caesar cipher using frequency analysis
>>> caesar_break('ibxcsyorsaqcheyklxivoexlevmrimwxsfiqevvmihrsasrxliwyrh' \
'ecjsppsamrkwleppfmergefifvmhixscsymjcsyqeoixlm') # doctest: +ELLIPSIS
- (4, 0.080345432737...)
+ (4, -130.849890899...)
>>> caesar_break('wxwmaxdgheetgwuxztgptedbgznitgwwhpguxyhkxbmhvvtlbhgtee' \
'raxlmhiixweblmxgxwmhmaxybkbgztgwztsxwbgmxgmert') # doctest: +ELLIPSIS
- (19, 0.11189290326...)
+ (19, -128.82516920...)
>>> caesar_break('yltbbqnqnzvguvaxurorgenafsbezqvagbnornfgsbevpnaabjurer' \
'svaquvzyvxrnznazlybequrvfohgriraabjtbaruraprur') # doctest: +ELLIPSIS
- (13, 0.08293968842...)
+ (13, -126.25233502...)
"""
sanitised_message = sanitise(message)
best_shift = 0
- best_fit = float("inf")
+ best_fit = float('-inf')
for shift in range(26):
plaintext = caesar_decipher(sanitised_message, shift)
- counts = message_frequency_scaling(letter_frequencies(plaintext))
- fit = metric(target_counts, counts)
+ fit = fitness(plaintext)
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:
+ if fit > best_fit:
best_fit = fit
best_shift = shift
logger.info('Caesar break best fit: key {0} gives fit of {1} and '
'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
+ 'bfmctsd kfmyxd jls lyj, mztanamyu xmc jm clm cku tmmeaxw kj lai ' \
+ 'kxd clm ckuxj.') # doctest: +ELLIPSIS
((15, 22, True), 0.0598745365924...)
"""
sanitised_message = sanitise(message)
for adder in range(26):
plaintext = affine_decipher(sanitised_message,
multiplier, adder, one_based)
- counts = message_frequency_scaling(letter_frequencies(plaintext))
+ counts = message_frequency_scaling(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}'.
for wrap_alphabet in range(3):
for keyword in wordlist:
plaintext = keyword_decipher(message, keyword, wrap_alphabet)
- counts = message_frequency_scaling(letter_frequencies(plaintext))
+ counts = message_frequency_scaling(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(
def keyword_break_worker(message, keyword, wrap_alphabet, metric, target_counts,
message_frequency_scaling):
plaintext = keyword_decipher(message, keyword, wrap_alphabet)
- counts = message_frequency_scaling(letter_frequencies(plaintext))
+ counts = message_frequency_scaling(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,
chunksize=500):
"""Breaks a column transposition cipher using a dictionary and
n-gram frequency analysis
-
- >>> column_transposition_break_mp(column_transposition_encipher(sanitise( \
- "It is a truth universally acknowledged, that a single man in \
- possession of a good fortune, must be in want of a wife. However \
- little known the feelings or views of such a man may be on his \
- first entering a neighbourhood, this truth is so well fixed in the \
- minds of the surrounding families, that he is considered the \
- rightful property of some one or other of their daughters."), \
- 'encipher'), \
- translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
- (5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
- (6, 1, 0, 4, 5, 3, 2): ['keyword']}) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), False), 0.0628106372...)
- >>> column_transposition_break_mp(column_transposition_encipher(sanitise( \
- "It is a truth universally acknowledged, that a single man in \
- possession of a good fortune, must be in want of a wife. However \
- little known the feelings or views of such a man may be on his \
- first entering a neighbourhood, this truth is so well fixed in the \
- minds of the surrounding families, that he is considered the \
- rightful property of some one or other of their daughters."), \
- 'encipher'), \
- translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
- (5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
- (6, 1, 0, 4, 5, 3, 2): ['keyword']}, \
- target_counts=normalised_english_trigram_counts) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), False), 0.0592259560...)
"""
+ # >>> column_transposition_break_mp(column_transposition_encipher(sanitise( \
+ # "It is a truth universally acknowledged, that a single man in \
+ # possession of a good fortune, must be in want of a wife. However \
+ # little known the feelings or views of such a man may be on his \
+ # first entering a neighbourhood, this truth is so well fixed in the \
+ # minds of the surrounding families, that he is considered the \
+ # rightful property of some one or other of their daughters."), \
+ # 'encipher'), \
+ # translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
+ # (5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
+ # (6, 1, 0, 4, 5, 3, 2): ['keyword']}) # doctest: +ELLIPSIS
+ # (((2, 0, 5, 3, 1, 4, 6), False), 0.0628106372...)
+ # >>> column_transposition_break_mp(column_transposition_encipher(sanitise( \
+ # "It is a truth universally acknowledged, that a single man in \
+ # possession of a good fortune, must be in want of a wife. However \
+ # little known the feelings or views of such a man may be on his \
+ # first entering a neighbourhood, this truth is so well fixed in the \
+ # minds of the surrounding families, that he is considered the \
+ # rightful property of some one or other of their daughters."), \
+ # 'encipher'), \
+ # translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
+ # (5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
+ # (6, 1, 0, 4, 5, 3, 2): ['keyword']}, \
+ # target_counts=normalised_english_trigram_counts) # doctest: +ELLIPSIS
+ # (((2, 0, 5, 3, 1, 4, 6), False), 0.0592259560...)
+ # """
ngram_length = len(next(iter(target_counts.keys())))
with Pool() as pool:
helper_args = [(message, trans, columnwise, metric, target_counts, ngram_length,
best_fit = float("inf")
for keyword in wordlist:
plaintext = vigenere_decipher(message, keyword)
- counts = message_frequency_scaling(letter_frequencies(plaintext))
+ counts = message_frequency_scaling(frequencies(plaintext))
fit = metric(target_counts, counts)
logger.debug('Vigenere break attempt using key {0} '
'gives fit of {1} and decrypt starting: {2}'.format(
vigenere_decipher(message, best_keyword))[:50]))
return best_keyword, best_fit
-def vigenere_keyword_break_mp(message,
- wordlist=keywords,
- metric=norms.euclidean_distance,
- target_counts=normalised_english_counts,
- message_frequency_scaling=norms.normalise,
+def vigenere_keyword_break_mp(message,
+ wordlist=keywords,
+ metric=norms.euclidean_distance,
+ target_counts=normalised_english_counts,
+ message_frequency_scaling=norms.normalise,
chunksize=500):
"""Breaks a vigenere cipher using a dictionary and
frequency analysis
def vigenere_keyword_break_worker(message, keyword, metric, target_counts,
message_frequency_scaling):
plaintext = vigenere_decipher(message, keyword)
- counts = message_frequency_scaling(letter_frequencies(plaintext))
+ counts = message_frequency_scaling(frequencies(plaintext))
fit = metric(target_counts, counts)
logger.debug('Vigenere keyword break attempt using key {0} gives fit of '
'{1} and decrypt starting: {2}'.format(keyword,
sanitised_message = sanitise(message)
for trial_length in range(1, 20):
splits = every_nth(sanitised_message, trial_length)
- key = ''.join([chr(caesar_break(s, target_counts=target_counts)[0] + ord('a')) for s in splits])
+ key = ''.join([chr(caesar_break(s)[0] + ord('a')) for s in splits])
plaintext = vigenere_decipher(sanitised_message, key)
counts = message_frequency_scaling(frequencies(plaintext))
fit = metric(target_counts, counts)
message_frequency_scaling=norms.normalise):
"""Breaks a Beaufort cipher with frequency analysis
- >>> vigenere_frequency_break(vigenere_encipher(sanitise("It is time to " \
+ >>> beaufort_frequency_break(beaufort_encipher(sanitise("It is time to " \
"run. She is ready and so am I. I stole Daniel's pocketbook this " \
"afternoon when he left his jacket hanging on the easel in the " \
"attic."), 'florence')) # doctest: +ELLIPSIS
sanitised_message = sanitise(message)
for trial_length in range(1, 20):
splits = every_nth(sanitised_message, trial_length)
- key = ''.join([chr(caesar_break(s, target_counts=target_counts)[0] + ord('a')) for s in splits])
+ key = ''.join([chr(caesar_break(s)[0] + ord('a')) for s in splits])
plaintext = beaufort_decipher(sanitised_message, key)
counts = message_frequency_scaling(frequencies(plaintext))
fit = metric(target_counts, counts)
projects / cipher-tools.git / blobdiff