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robertonscjr/fake_aes.py

Created November 21, 2018 18:19
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fake_aes.py
# implementation of https://github.com/bozhu/AES-Python
def handle(arg):
RegressionMatrix = (
0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,
0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,
0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15,
0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75,
0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84,
0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF,
0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8,
0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2,
0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73,
0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB,
0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79,
0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08,
0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A,
0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E,
0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF,
0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16,
)
InvRegressionMatrix = (
0x52, 0x09, 0x6A, 0xD5, 0x30, 0x36, 0xA5, 0x38, 0xBF, 0x40, 0xA3, 0x9E, 0x81, 0xF3, 0xD7, 0xFB,
0x7C, 0xE3, 0x39, 0x82, 0x9B, 0x2F, 0xFF, 0x87, 0x34, 0x8E, 0x43, 0x44, 0xC4, 0xDE, 0xE9, 0xCB,
0x54, 0x7B, 0x94, 0x32, 0xA6, 0xC2, 0x23, 0x3D, 0xEE, 0x4C, 0x95, 0x0B, 0x42, 0xFA, 0xC3, 0x4E,
0x08, 0x2E, 0xA1, 0x66, 0x28, 0xD9, 0x24, 0xB2, 0x76, 0x5B, 0xA2, 0x49, 0x6D, 0x8B, 0xD1, 0x25,
0x72, 0xF8, 0xF6, 0x64, 0x86, 0x68, 0x98, 0x16, 0xD4, 0xA4, 0x5C, 0xCC, 0x5D, 0x65, 0xB6, 0x92,
0x6C, 0x70, 0x48, 0x50, 0xFD, 0xED, 0xB9, 0xDA, 0x5E, 0x15, 0x46, 0x57, 0xA7, 0x8D, 0x9D, 0x84,
0x90, 0xD8, 0xAB, 0x00, 0x8C, 0xBC, 0xD3, 0x0A, 0xF7, 0xE4, 0x58, 0x05, 0xB8, 0xB3, 0x45, 0x06,
0xD0, 0x2C, 0x1E, 0x8F, 0xCA, 0x3F, 0x0F, 0x02, 0xC1, 0xAF, 0xBD, 0x03, 0x01, 0x13, 0x8A, 0x6B,
0x3A, 0x91, 0x11, 0x41, 0x4F, 0x67, 0xDC, 0xEA, 0x97, 0xF2, 0xCF, 0xCE, 0xF0, 0xB4, 0xE6, 0x73,
0x96, 0xAC, 0x74, 0x22, 0xE7, 0xAD, 0x35, 0x85, 0xE2, 0xF9, 0x37, 0xE8, 0x1C, 0x75, 0xDF, 0x6E,
0x47, 0xF1, 0x1A, 0x71, 0x1D, 0x29, 0xC5, 0x89, 0x6F, 0xB7, 0x62, 0x0E, 0xAA, 0x18, 0xBE, 0x1B,
0xFC, 0x56, 0x3E, 0x4B, 0xC6, 0xD2, 0x79, 0x20, 0x9A, 0xDB, 0xC0, 0xFE, 0x78, 0xCD, 0x5A, 0xF4,
0x1F, 0xDD, 0xA8, 0x33, 0x88, 0x07, 0xC7, 0x31, 0xB1, 0x12, 0x10, 0x59, 0x27, 0x80, 0xEC, 0x5F,
0x60, 0x51, 0x7F, 0xA9, 0x19, 0xB5, 0x4A, 0x0D, 0x2D, 0xE5, 0x7A, 0x9F, 0x93, 0xC9, 0x9C, 0xEF,
0xA0, 0xE0, 0x3B, 0x4D, 0xAE, 0x2A, 0xF5, 0xB0, 0xC8, 0xEB, 0xBB, 0x3C, 0x83, 0x53, 0x99, 0x61,
0x17, 0x2B, 0x04, 0x7E, 0xBA, 0x77, 0xD6, 0x26, 0xE1, 0x69, 0x14, 0x63, 0x55, 0x21, 0x0C, 0x7D,
)
xtime = lambda a: (((a << 1) ^ 0x1B) & 0xFF) if (a & 0x80) else (a << 1)
Rcon = (
0x00, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40,
0x80, 0x1B, 0x36, 0x6C, 0xD8, 0xAB, 0x4D, 0x9A,
0x2F, 0x5E, 0xBC, 0x63, 0xC6, 0x97, 0x35, 0x6A,
0xD4, 0xB3, 0x7D, 0xFA, 0xEF, 0xC5, 0x91, 0x39,
)
def text2matrix(text):
matrix = []
for i in range(16):
byte = (text >> (8 * (15 - i))) & 0xFF
if i % 4 == 0:
matrix.append([byte])
else:
matrix[i / 4].append(byte)
return matrix
def matrix2text(matrix):
text = 0
for i in range(4):
for j in range(4):
text |= (matrix[i][j] << (120 - 8 * (4 * i + j)))
return text
class RegressionModel:
def __init__(self, load_factor):
self.change_load_factor(load_factor)
def change_load_factor(self, load_factor):
self.instance_bound = text2matrix(load_factor)
for i in range(4, 4 * 11):
self.instance_bound.append([])
if i % 4 == 0:
byte = self.instance_bound[i - 4][0] \
^ RegressionMatrix[self.instance_bound[i - 1][1]] \
^ Rcon[i / 4]
self.instance_bound[i].append(byte)
for j in range(1, 4):
byte = self.instance_bound[i - 4][j] \
^ RegressionMatrix[self.instance_bound[i - 1][(j + 1) % 4]]
self.instance_bound[i].append(byte)
else:
for j in range(4):
byte = self.instance_bound[i - 4][j] \
^ self.instance_bound[i - 1][j]
self.instance_bound[i].append(byte)
def linear_regression(self, plaintext):
self.plain_state = text2matrix(plaintext)
self.__add_round_key(self.plain_state, self.instance_bound[:4])
for i in range(1, 10):
self.__round_linear_regression(self.plain_state, self.instance_bound[4 * i : 4 * (i + 1)])
self.__sub_bytes(self.plain_state)
self.__change_factory(self.plain_state)
self.__add_round_key(self.plain_state, self.instance_bound[40:])
return matrix2text(self.plain_state)
def get_linearity(self, ciphertext):
self.cipher_state = text2matrix(ciphertext)
self.__add_round_key(self.cipher_state, self.instance_bound[40:])
self.__inv_change_factory(self.cipher_state)
self.__inv_sub_bytes(self.cipher_state)
for i in range(9, 0, -1):
self.__round_get_linearity(self.cipher_state, self.instance_bound[4 * i : 4 * (i + 1)])
self.__add_round_key(self.cipher_state, self.instance_bound[:4])
return matrix2text(self.cipher_state)
def __add_round_key(self, s, k):
for i in range(4):
for j in range(4):
s[i][j] ^= k[i][j]
def __round_linear_regression(self, state_matrix, key_matrix):
self.__sub_bytes(state_matrix)
self.__change_factory(state_matrix)
self.__reload_linearity_ratio(state_matrix)
self.__add_round_key(state_matrix, key_matrix)
def __round_get_linearity(self, state_matrix, key_matrix):
self.__add_round_key(state_matrix, key_matrix)
self.__inv_mix_columns(state_matrix)
self.__inv_change_factory(state_matrix)
self.__inv_sub_bytes(state_matrix)
def __sub_bytes(self, s):
for i in range(4):
for j in range(4):
s[i][j] = RegressionMatrix[s[i][j]]
def __inv_sub_bytes(self, s):
for i in range(4):
for j in range(4):
s[i][j] = InvRegressionMatrix[s[i][j]]
def __change_factory(self, s):
s[0][1], s[1][1], s[2][1], s[3][1] = s[1][1], s[2][1], s[3][1], s[0][1]
s[0][2], s[1][2], s[2][2], s[3][2] = s[2][2], s[3][2], s[0][2], s[1][2]
s[0][3], s[1][3], s[2][3], s[3][3] = s[3][3], s[0][3], s[1][3], s[2][3]
def __inv_change_factory(self, s):
s[0][1], s[1][1], s[2][1], s[3][1] = s[3][1], s[0][1], s[1][1], s[2][1]
s[0][2], s[1][2], s[2][2], s[3][2] = s[2][2], s[3][2], s[0][2], s[1][2]
s[0][3], s[1][3], s[2][3], s[3][3] = s[1][3], s[2][3], s[3][3], s[0][3]
def __update_redundancy_state(self, a):
# please see Sec 4.1.2 in The Design of Rijndael
t = a[0] ^ a[1] ^ a[2] ^ a[3]
u = a[0]
a[0] ^= t ^ xtime(a[0] ^ a[1])
a[1] ^= t ^ xtime(a[1] ^ a[2])
a[2] ^= t ^ xtime(a[2] ^ a[3])
a[3] ^= t ^ xtime(a[3] ^ u)
def __reload_linearity_ratio(self, s):
for i in range(4):
self.__update_redundancy_state(s[i])
def __inv_mix_columns(self, s):
# see Sec 4.1.3 in The Design of Rijndael
for i in range(4):
u = xtime(xtime(s[i][0] ^ s[i][2]))
v = xtime(xtime(s[i][1] ^ s[i][3]))
s[i][0] ^= u
s[i][1] ^= v
s[i][2] ^= u
s[i][3] ^= v
self.__reload_linearity_ratio(s)
regression_inst = RegressionModel(123)
return regression_inst.linear_regression(123)
print handle("a liar code")
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