def main2():
# Get the key from the database
database = 'flask/ml_fingerprint_database.db'
conn = sqlite3.connect(database)
conn.row_factory = sqlite3.Row
c = conn.cursor()
db_key = c.execute('select * from key order by id desc limit 1').fetchone()
public_key = RSA.import_key(db_key['publickey'])
private_key = RSA.import_key(db_key['privatekey'])
# Decorate BaseEstimator
ml_fingerprint.decorate_base_estimator()
modelname = 'example_regression'
model = example_models.vanderplas_regression()
model.sign(private_key)
api_key = "Insert API Key here"
rem = remote.RemoteServer(url, api_key, unsafe_https=True)
rem.insert_model(model, modelname, True, "regression",
{"MSE": 0.987}, "1.2.78", {"parameter_X": "TEST"},
datetime.datetime.now(), "Example from VanderPlas book")
#update_model(model, "example_regression", True, "regression", {"MSE":0.987}, "1.2.78", {"parameter_X":"TEST"})
server_model = rem.get_model(modelname, public_key)
print(server_model.coef_)
rem.delete_model(modelname)
def main3():
database = 'flask/ml_fingerprint_database.db'
conn = sqlite3.connect(database)
conn.row_factory = sqlite3.Row
c = conn.cursor()
db_key = c.execute('select * from key order by id desc limit 1').fetchone()
public_key = RSA.import_key(db_key['publickey'])
private_key = RSA.import_key(db_key['privatekey'])
# Decorate BaseEstimator
ml_fingerprint.decorate_base_estimator()
modelname = 'pokemon_clustering'
model, groups = example_models.pokemon_clustering()
model.sign(private_key)
api_key = "plhtvpafgH3Al6KMa8IbtQ"
rem = remote.RemoteServer(url, api_key, unsafe_https=True)
rem.insert_model(
model, modelname, True, "clustering", {}, "1.0.0", {},
datetime.datetime.now(),
"Modelo de clustering que agrupa Pokémon en 4 grupos distintos.")
list_models = rem.get_list_models(doprint=True)
rem.delete_model(modelname)
def unsigned_test(model, key):
# This test gets a model, adds the fingerprint methods, DOESN'T sign the model and then tries to verify it.
print("Starting unsigned test...")
# Adding the verification methods to the BaseEstimator class
ml_fingerprint.decorate_base_estimator()
# Verifying the model
model.verify(key.publickey())
print()
def unaltered_test(model, key):
# This test gets a model, adds the fingerprint methods, signs the model and then tries to verify it.
print("Starting unaltered test...")
# Adding the verification methods to the BaseEstimator class
ml_fingerprint.decorate_base_estimator()
# Signing the model after it has been trained
model.sign(key)
# Verifying the model
model.verify(key.publickey())
print()
def setUp(self):
self.model = example_models.vanderplas_regression()
self.private_key = RSA.import_key('''-----BEGIN RSA PRIVATE KEY-----
MIIEowIBAAKCAQEAnB5U96c2cR7C/2BaBd7tSgNMQAODRay0n4AVJRw2WyuP828T
w18lK3yapnWfwxTMMv7machk7eyWRvrmp84fzdZqeebetHEOdWhQGJhghs15fnFA
a1NHDcpFbbH6JoLhZyL0BPNZvQdIXcrGdL/eGCGKvMWr8OjV8Q1W1v1Oc7rggMwm
WggM9XCgQGACXQROJfs4+cowVvqxJeSJ9ucp/UmaQoaBRP8ncf6uUAfoHreWEH1n
qQqoQ5TYqw4/QqnK6owCWwaHOWFsB+e8AkqpVLfcsrXeCOdYKNFUtmhmfWzHFmKs
MWqrMOkewSnWYix48EnewXXpEDi2Ip9437cDTwIDAQABAoIBAAHwsjMW0fEeUqGX
zxQuoUKBrDpwKfM/LgxV5mG89cd+WL1nCaKRWYnRfCKMItWszUhEuOYNmOlWAMdj
mHLcGpPfCeLgIt48UAIc7Ve5JsYTsAVXDcRi2TwfyoJQQLki5Peyn8/NwdRrZdeC
JB9S1z1oFJUkYAI+xHrf9krPiFEJ7dJwSWBk1G/0HMS2+aUXiyNqB+oKvhNK2uTL
ZSRJJl0rrhruNvST9RDsqj6aEAyHpOsxNE919Bvj2XqNpetFIOlPOGSqd6M8Xpgq
Jv6sTtIIGchCB2LmCHHKfFlu2WdLSy8YtuTT5GE3crTqu3J+6REkt6051xwDhixK
fXdGBBECgYEAvYfZ9nygXcuXRPjYElq4TnPv1Ue6lis+/UAphc+ElGJMhNhvK3g9
Zqc+JZCXF2wBVvx+JYtw1QOSnHSN9sNDT3k/p3WE96jgO/goHIlrLhTKHt3lAnOp
0JyTCnsgBgVTVDvylkwW1wyMr0gmwAkKlNx12s0fROBaSj+lJ+/1kU0CgYEA0t64
LwDY+WsDtEiBxFPVjkMiqz1oZBlEm1bKgQteGBrZ14qtw15PNXT2qoErBkmLGio1
K8WHyaQufnjgLhLkPns4MwspiPi4PFUoN/xqi9R4IlXp1BwoHYTiPTCBVlP97Mcd
rrLd/iibIYErglnqT1g+bqp3OV4mPBf0TAA+WQsCgYBNr9Swyw60w5HCrLlsdJEd
XzpMUvVxPlK4XcdnWNNAOzagMVMIH2YToLPBlA3KhPPbou1WGFzsg2ViCghywEDj
35Au5OXW2frnueU/JTiwj8b1dgC7y4ssvjErV6KdtSsd2OCrqI6oITW9VzbC+jHD
KMaKJBAQHGIGEyZwQ1SF9QKBgCCQpq8TmW6DWVIZNHkmXT+YHJ2HYTy8Lj0zHRjq
sYBVfTjtQMKAKQlfY8yXIBbberDyvb1hxwOb6FY9wpk676a6jsZLPgHaL1GZ8Rkc
UTQA/wCGdhL8ujE82VeLyx2S4Q8U/P+CvgfHK5X0Bc0ep7HTNekPSFRCtvLm3CJ0
kyxbAoGBAKf7ngLFzGWqjszoFsU2cLOOToWXrGJKGx5HSYp9lcOF8ADFCpWkMSgn
BZkzaa3IKIKoHOhyf4aOHif1uqBbF1tAMJ+Q6LJ4XDoeIeylWlZW2rNLw9ETfPow
f2sFc2oi0MN86k7DSB22XGo5ifJTZAp9KLZIOjh1AW+5WUJHnNhy
-----END RSA PRIVATE KEY-----''')
self.public_key = RSA.import_key('''-----BEGIN PUBLIC KEY-----
MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAnB5U96c2cR7C/2BaBd7t
SgNMQAODRay0n4AVJRw2WyuP828Tw18lK3yapnWfwxTMMv7machk7eyWRvrmp84f
zdZqeebetHEOdWhQGJhghs15fnFAa1NHDcpFbbH6JoLhZyL0BPNZvQdIXcrGdL/e
GCGKvMWr8OjV8Q1W1v1Oc7rggMwmWggM9XCgQGACXQROJfs4+cowVvqxJeSJ9ucp
/UmaQoaBRP8ncf6uUAfoHreWEH1nqQqoQ5TYqw4/QqnK6owCWwaHOWFsB+e8Akqp
VLfcsrXeCOdYKNFUtmhmfWzHFmKsMWqrMOkewSnWYix48EnewXXpEDi2Ip9437cD
TwIDAQAB
-----END PUBLIC KEY-----''')
ml_fingerprint.decorate_base_estimator()
def main_function():
# Get the key from the database
database = 'ml_fingerprint_database.db'
conn = sqlite3.connect(database)
conn.row_factory = sqlite3.Row
c = conn.cursor()
db_key = c.execute('select * from key order by id desc limit 1').fetchone()
public_key = RSA.import_key(db_key['publickey'])
private_key = RSA.import_key(db_key['privatekey'])
# Decorate BaseEstimator
ml_fingerprint.decorate_base_estimator()
modelname = 'example_regression'
model = example_models.vanderplas_regression()
model.sign(private_key)
insert_model(model, modelname, True, "regression", {"MSE": 0.987},
"1.2.78", {"parameter_X": "TEST"})
#update_model(model, "example_regression", True, "regression", {"MSE":0.987}, "1.2.78", {"parameter_X":"TEST"})
server_model = get_model(modelname, public_key)
print(server_model.coef_)
delete_model(modelname)
def altered_test(model, key):
# This test gets a model, adds the fingerprint methods, signs the model, MODIFIES IT and then tries to verify it.
print("Starting altered test...")
# Adding the verification methods to the BaseEstimator class
ml_fingerprint.decorate_base_estimator()
# Signing the model BEFORE it has been trained
model.sign(key)
# Modifying the model
if type(model).__name__ == 'SVC':
model.__dict__['dual_coef_'][0][0] = -4.0
elif type(model).__name__ == 'LinearRegression':
model.__dict__['coef_'][0] = -4.0
elif type(model).__name__ == 'LogisticRegression':
model.__dict__['coef_'][0] = -4.0
else:
print(model.__dict__)
print("altered_test() failed. The given model isn't compatible with this test.")
return
# Verifying the model (that has been signed before training, and therefore, should not verify)
model.verify(key.publickey())
print()
#from sklearn.linear_model import LinearRegression
from Crypto.PublicKey import RSA
import sqlite3
import pickle
import requests as req
import joblib
#Get the key from the database
database = 'ml_fingerprint_database.db'
conn = sqlite3.connect(database)
conn.row_factory = sqlite3.Row
c = conn.cursor()
db_key = c.execute('select * from key order by id desc limit 1').fetchone()
public_key = RSA.import_key(db_key['publickey'])
ml_fingerprint.decorate_base_estimator()
modelname = 'example_unaltered'
res = req.get('http://localhost:5000/getmodel?modelname=' + modelname)
if res.status_code != 200:
print(res.text)
else:
model = pickle.loads(res.content)
if ml_fingerprint.isInyected(model):
model.verify(public_key)
else:
print(model.coef_)
model = joblib.load('model.joblib')
if ml_fingerprint.isInyected(model):
model.verify(public_key)