164 lines
8.1 KiB
Python
Executable File
164 lines
8.1 KiB
Python
Executable File
#!/usr/bin/env python3
|
||
# -*- coding: UTF-8 -*-
|
||
import LP
|
||
|
||
INVERT = False
|
||
KEY_MAX_SCORE = 0.05
|
||
AFF_MAX_SCORE = 0.04
|
||
IRP_F_ONLY = True
|
||
session_files = []
|
||
|
||
|
||
#########################################
|
||
# Perform heuristic search on the keylength, interrupts, and key
|
||
#########################################
|
||
|
||
def break_cipher(fname, candidates, solver, key_fn):
|
||
def fn_similarity(x):
|
||
return LP.Probability(x).similarity()
|
||
|
||
filename = LP.path.page(fname)
|
||
slvr = solver()
|
||
slvr.input.load(file=filename)
|
||
slvr.output.QUIET = True
|
||
slvr.output.COLORS = False
|
||
slvr.KEY_INVERT = INVERT
|
||
key_max_score = KEY_MAX_SCORE
|
||
if key_fn.__name__ == 'GuessAffine':
|
||
key_max_score = AFF_MAX_SCORE
|
||
for irp_count, score, irp, kl, skips in candidates:
|
||
if IRP_F_ONLY and irp != 0:
|
||
continue
|
||
data = LP.load_indices(filename, irp, maxinterrupt=irp_count)
|
||
if INVERT:
|
||
data = [28 - x for x in data]
|
||
iguess = LP.SearchInterrupt(data, (28 - irp) if INVERT else irp)
|
||
print('IoC: {}, interrupt: {}, count: {}, solver: {}'.format(
|
||
score, LP.RUNES[irp], len(iguess.stops), key_fn.__name__))
|
||
testcase = iguess.join(iguess.from_occurrence_index(skips))
|
||
|
||
key_score, key = key_fn(testcase).guess(kl, fn_similarity)
|
||
if key_score > key_max_score:
|
||
continue
|
||
prio = (1 - key_score) * max(0, score)
|
||
print(f' key_score: {prio:.4f}, {key}')
|
||
print(' skip:', skips)
|
||
txtname = f'{fname}_{prio:.4f}.{key_fn.__name__}.{irp}_{kl}'
|
||
if INVERT:
|
||
txtname += '.inv'
|
||
while txtname in session_files:
|
||
txtname += '.'
|
||
session_files.append(txtname)
|
||
outfile = LP.path.tmp(txtname)
|
||
with open(outfile, 'w') as f:
|
||
f.write(
|
||
f'{irp}, {kl}, {score:.4f}, {key_score:.4f}, {key}, {skips}\n')
|
||
slvr.output.file_output = outfile
|
||
slvr.INTERRUPT = LP.RUNES[irp]
|
||
slvr.INTERRUPT_POS = skips
|
||
slvr.KEY_DATA = key
|
||
slvr.run()
|
||
|
||
|
||
def pattern_solver(fname, irp=0):
|
||
with open(LP.path.page(fname), 'r') as f:
|
||
orig = LP.RuneText(f.read())
|
||
# orig = LP.RuneText('ᛄᚹᚻᛗᛋᚪ-ᛋᛁᚫᛇ-ᛋᛠᚾᛞ-ᛇᛞ-ᛞᚾᚣᚹᛗ.ᛞᛈ-ᛝᛚᚳᚾᛗᚾᚣ-ᛖᛝᛖᚦᚣᚢ-ᚱᚻᛁᚠ-ᛟᛝ-ᛚᛖᚫᛋᛚᚳᛋᛇ.ᚣᚾᚻᛄᚾᚳᛡ-ᚷᚳᛝ-ᛈᛝ-ᛡᚷᚦᚷᛖ.ᚻᛠᚣ-ᛄᛞᚹᛒ-ᛇᛄᛝᚩᛟ.ᛗᛠᚣᛋᛖᛚᚠ-ᚾᚫᛁ-ᛄᚹᚻᚻᛚᛈᚹᚠ-ᚫᚩᛡᚫᛟ-ᚷᛠ-ᚪᛡᚠᛄᚱᛏᚢᛈ.ᛏᛈ-ᛇᛞ-ᛟᛗᛇᛒᛄᚳᛈ.ᛉᛟ-ᛒᚻᚱᛄᚣ-ᚾᚱ-ᚾᛡᛈᛈ-ᛚᛉᛗᛞ-ᛟᛝ-ᚷᛁᚱᚩᚹᛗ-ᚠᛇᚣ-ᚣᛝᛒ-ᛁ-ᚠᚾᚹᚢ-ᛠᚾᛈᚠᚻ.ᚫᛋᛄᚪᚻ-ᛒᛖᛋᚻᛠ-ᛄᛗ-ᛟᛡᚹᚪᛡ-ᛄᛋᛖᚢᛗ-ᛏᛖᛉᚪ-ᛞᛟᛉᚾᚠ-ᚱᛡᛒᛚᚩᛈᛝ-ᛋᛄᛚᛗ-ᛞᚱᛗᛗ-ᛒᛈ-ᛁᛉᚱᛄᛝ.ᛋᛇᚪ-ᛗᚠᚻᚣᚹᛉᛞ-ᛡᛁᚷᚪ-ᚩᚱ-ᚪᚾᚹᛇᛋᛞᛄᚷ-ᛡ-ᛖᚫᛄ-ᛞᛟᛁᚻᚹᛝ-ᛠᛈᛏ-ᚪᛗᛗᛚᛚᚪᛞ.ᛁᛠᛈᚷᛞ-ᛗᚣᛄᚳᚹᛚ-ᚻᛋᛟᛗ-ᚣᚫᛝᛚ-ᛠᛁᛝᛝᚪ-ᚳᛗ-ᚢᚫᛋ-ᛉᛠᚱ-ᛇᛡᛄᚻᛗᚾ-ᚻᛗᛝᛚ-ᛇᛞ-ᛟᚢᚣᚪᚷᚱ-ᛡᚷ-ᚷᛠ-ᛚᚻᛒ.ᛡᛒ-ᚩᛁᛄ-ᛗᛟᛉᚩᚣ-ᛞᚩ-ᚳᛗ-ᚾᛗᚩ-ᚷᛠ-ᛚᚱᚠᚷ-ᛁᚫᛗᛉ-ᛁᛠᚹᛚ-ᛖᛝᚾᛟᛗᚾ-ᛄᚾ-ᚾᚳᛚᛝ-ᛡ-ᚷᛞᛗᚱᚻᚩ-ᛗᛞᛠᚫᛞ-ᛞᚱᛗᛗ-ᚣᚪ-ᛗᛉᚢᛞᛇᚹ-ᛟᚱᛏᚱᛟᚢᛉᛗᛚᛈᛉᛝ.ᛏᛖ-ᛗᛋᚣ-ᚹᛁ-ᚹᛝ-ᛋᛇᛄᚳᛁᛋᛝ.ᛄᛚᚹ-ᚷᚠᛝ-ᚫᚷᛚᛡᛁᛡ.ᛖᚠᚣ-ᛉᛝᚻᛄᚾᛈᚠ-ᛉᚣ-ᛚᛄᛞᛝᛞᚪ-ᚩᛈ-ᚻᛟ-ᛖᚻᚱᚹ-ᛚᚷᚳ-ᛒᛈᛏᚻ-ᚠᛋᛠᚣᛋᚠ-ᛏᚷᛈᚪᛒ.')
|
||
# orig = LP.RuneText('ᛇᚦ-ᛒᛏᚣᚳ-ᛇᛚᛉ-ᛄᛚᚦᚪ-ᛋᚱᛉᚦ-ᚦᛄᚻ-ᛉᛗ-ᛏᛞᛋ-ᚣᚾ-ᚣᛟᛇᛈᛟᛚ-ᛈᚹᛚᚪᛗ-ᚪᛉᛁᛇᛝᚢᚱᛉ.ᛞᛄ-ᚻᛠᚪᛚ.ᚠᛚ-ᚩᛋᚾ-ᚫᛞᛋᛁᛞᚹᚾᚪᚪ-ᚱᛟᚻ-ᛚᚠᛚᚳᛟᚱ-ᚣᛏ-ᚹᛏᛝᚣ-ᚳᚩ-ᛄᚷᛟ-ᛖ-ᚫᚻᚦᛠ-ᛒᛠᛁ-ᛁᚩᛡ-ᛗᛉᚠᚷᛁ-ᚣᚣᛋᛇᛗᛠᚹ.ᛇᚪ-ᛇᛉᛡ-ᛄᚾᛇᛁᛇᚫ-ᛋᚱᚹ-ᛝᚣᚦ-ᛠᛁᛄᛚᚢᛄ-ᚻᛇᛚᛟ-ᛒᛠᛒᛚ-ᚩᛈᛈ-ᚢᚻᛚ-ᛡᚾᛚ-ᛒᚦᚱᚠᚦᚫ-ᛞᚳ-ᛄᚳᚷ-ᚹᚫ-ᚱᛉᚣᛖᚱ.ᛒᛝᚹ-ᛟᚳᚫᚹᛈᚢ-ᚱᛋᛒ-ᚷᚦᚳᛏᛏᛠᚹ-ᚱᚣᛞ-ᚣᛠᛄ-ᛋ-qᚪᛚᚾᛖᛄᚪ-ᛇᚻᛖ-ᛏᛠᛈ-ᛝᛉᚾᚳ-ᛋᚾᚹᚦᚾ-ᚣᛞᛝᚣ-ᛠᛠᛡ-ᛉᛁᛚᚢᚩ.ᛗᛉᚦ-ᛒᛝᛇᛠᛟ-ᛁᛟᛏ-ᛠᛏᛄ-ᚫᚳᛉᛝᛖᚠ-ᛇᚠ.ᛄᛄᛝᛟᛡᛟ-ᛠᛖᚫ-ᚦᛏᛠᛗ-ᛁᛏᚩᛒᛡ-ᛝᛟ-ᛉᚠᛇᚷᛗᛠ-ᚠᛖ-ᚳᛖᛖᚾᛠᛁᚪᛟ-ᛉᚣ-ᚢᛁ.ᛒᛏ.ᛒᛠ-ᛠᛁᚢᛗ-ᛞᛟᛋᛠᚷᚠᛇᚫ-ᛏᚪ-ᛇᚦ-ᛒᚪᛟᚩᛗ.ᛟᚳᛇ-ᛞᛞ-ᛋᚱᛁᛋᚦ-ᛇᛒ-ᚳᛒᛟ-ᚳᛟᚳᚷᛇ.ᛗᛉᚦ-ᛞᚦᛉᛈᛚᛈᛚᛁᚢ-ᚳᛞᛡᛝᚻᚷ-ᛞᚪ-ᚳᛟᚳᛁᛟᛞ-')
|
||
data = orig.index
|
||
if False: # longest uninterrupted text
|
||
pos, lg = LP.InterruptDB.longest_no_interrupt(data, interrupt=0, irpmax=0)
|
||
data = data[pos:pos + lg]
|
||
else: # from the beginning
|
||
data = data[:170]
|
||
|
||
data_i = [i for i, x in enumerate(data) if x == 29]
|
||
data = [x for x in data if x != 29]
|
||
|
||
def fn_similarity(x):
|
||
return LP.Probability(x).similarity()
|
||
|
||
def fn_pattern_mirror(x, kl):
|
||
for i in range(10000): # mirrored, 012210012210 or 012101210
|
||
yield from x
|
||
# yield from x[::-1]
|
||
yield from x[::-1][1:-1]
|
||
|
||
print(fname)
|
||
gr = LP.GuessPattern(data)
|
||
for kl in range(3, 19):
|
||
# for pattern_shift in range(1):
|
||
# fn_pattern = fn_pattern_mirror
|
||
for pattern_shift in range(1, kl):
|
||
def fn_pattern_shift(x, kl): # shift by (more than) one, 012201120
|
||
for i in range(10000):
|
||
yield from x[(i * pattern_shift) % kl:]
|
||
yield from x[:(i * pattern_shift) % kl]
|
||
|
||
fn_pattern = fn_pattern_shift
|
||
# Find proper pattern
|
||
res = []
|
||
for offset in range(kl): # up to keylen offset
|
||
mask = LP.GuessPattern.pattern(kl, fn_pattern)
|
||
parts = gr.split(kl, mask, offset)
|
||
score = sum(LP.Probability(x).IC() for x in parts) / kl
|
||
if score > 1.6 and score < 2.1:
|
||
res.append((score, parts, offset))
|
||
|
||
# Find best matching key for pattern
|
||
for score, parts, off in res:
|
||
sc, solution = LP.GuessPattern.guess(parts, fn_similarity)
|
||
if sc < 0.1:
|
||
fmt = 'kl: {}, pattern-n: {}, IoC: {:.3f}, dist: {:.4f}, offset: {}, key: {}'
|
||
print(fmt.format(kl, pattern_shift, score, sc, off,
|
||
LP.RuneText(solution).text))
|
||
solved = gr.zip(fn_pattern(solution, kl), off)
|
||
for i in data_i:
|
||
solved.insert(i, 29)
|
||
print(' ', LP.RuneText(solved).text)
|
||
|
||
|
||
#########################################
|
||
# main
|
||
#########################################
|
||
db = LP.InterruptDB.load('db_norm')
|
||
# IOC_MIN_SCORE = 1.4 # for db_high
|
||
IOC_MIN_SCORE = 0.55 # for db_norm
|
||
|
||
for fname in [
|
||
'p0-2', # ???
|
||
'p3-7', # ???
|
||
'p8-14', # ???
|
||
'p15-22', # ???
|
||
'p23-26', # ???
|
||
'p27-32', # ???
|
||
'p33-39', # ???
|
||
'p40-53', # ???
|
||
'p54-55', # ???
|
||
# '0_warning', # invert
|
||
# '0_welcome', # V8
|
||
# '0_wisdom', # plain
|
||
# '0_koan_1', # invert + shift
|
||
# '0_loss_of_divinity', # plain
|
||
# 'jpg107-167', # V13
|
||
# 'jpg229', # plain
|
||
# 'p56_an_end', # totient
|
||
# 'p57_parable', # plain
|
||
]:
|
||
pattern_solver(fname)
|
||
# break
|
||
continue
|
||
# NGramShifter().guess(data, 'ᚠ')
|
||
if fname not in db:
|
||
print(fname, 'not in db.')
|
||
continue
|
||
print()
|
||
print(f'loading file: pages/{fname}.txt')
|
||
candidates = [x for x in db[fname] if x[1] >= IOC_MIN_SCORE]
|
||
if not candidates:
|
||
maxscore = max(x[1] for x in db[fname])
|
||
print('No candidates. Highest score is only', maxscore)
|
||
continue
|
||
break_cipher(fname, candidates, LP.AffineSolver, LP.GuessAffine)
|
||
break_cipher(fname, candidates, LP.VigenereSolver, LP.GuessVigenere)
|