def test_corrtrain_correlation_multi(self):
from leakagemodels import LeakageModel
"""
Artificial example to test AICorrNet and trace processing with multiple leakage values and multiple subkeys.
"""
# ------------------------------
# Generate data
# ------------------------------
traces = [ # Contains abs(trace). Shape = [trace, point]
[1, 1, 1, -15],
[-4, 2, 2, -12],
[10, 3, 3, 8],
[8, 1, 1, -14],
[9, 0, -3, 8],
]
plaintexts = [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 15, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
keys = [
[0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
# Convert to numpy
traces = np.array(traces)
plaintexts = np.array(plaintexts)
keys = np.array(keys)
trace_set = TraceSet(name='test',
traces=traces,
plaintexts=plaintexts,
keys=keys)
# ------------------------------
# Preprocess data
# ------------------------------
conf = Namespace(
max_cache=0,
augment_roll=False,
augment_noise=False,
normalize=False,
traces_per_set=4,
online=False,
dataset_id='qa',
cnn=False,
leakage_model=LeakageModelType.AES_MULTI,
input_type=AIInputType.SIGNAL,
augment_shuffle=True,
n_hidden_layers=1,
n_hidden_nodes=256,
activation='leakyrelu',
metric_freq=100,
regularizer=None,
reglambda=0.001,
model_suffix=None,
use_bias=True,
batch_norm=True,
hamming=False,
key_low=1,
key_high=3,
loss_type='correlation',
lr=0.001,
epochs=5000,
batch_size=512,
norank=False,
)
it_dummy = AICorrSignalIterator([],
conf,
batch_size=10000,
request_id=None,
stream_server=None)
x, y = it_dummy._preprocess_trace_set(trace_set)
# ------------------------------
# Train and obtain encodings
# ------------------------------
model = ai.AICorrNet(conf, input_dim=4, name="test")
print(model.info())
rank_cb = rank.CorrRankCallback(conf,
'/tmp/deleteme/',
save_best=False,
save_path=None)
rank_cb.set_trace_set(trace_set)
if model.using_regularization:
print(
"Warning: cant do correlation loss test because regularizer will influence loss function"
)
return
# Find optimal weights
print("The x (EM samples) and y (leakage model values) are:")
print(x)
print(y)
print(
"When feeding x through the model without training, the encodings become:"
)
print(model.predict(x))
print("Training now")
model.train_set(x,
y,
save=False,
epochs=conf.epochs,
extra_callbacks=[rank_cb])
print("Done training")
# Get the encodings of the input data using the same approach used in ops.py corrtest (iterate over rows)
result = []
for i in range(0, x.shape[0]):
result.append(
model.predict(np.array([x[i, :]], dtype=float))[0]
) # Result contains sum of points such that corr with y[key_index] is maximal for all key indices. Shape = [trace, 16]
result = np.array(result)
print(
"When feeding x through the model after training, the encodings for key bytes %d to %d become:\n %s"
% (conf.key_low, conf.key_high, str(result)))
# ------------------------------
# Check loss function
# ------------------------------
# Evaluate the model to get the loss for the encodings
predicted_loss = model.model.evaluate(x, y, verbose=0)
# Manually calculate the loss using numpy to verify that we are learning a correct correlation
calculated_loss = 0
num_keys = (conf.key_high - conf.key_low)
num_outputs = LeakageModel.get_num_outputs(conf) // num_keys
for i in range(0, num_keys):
subkey_hws = y[:, i * num_outputs:(i + 1) * num_outputs]
subkey_encodings = result[:, i * num_outputs:(i + 1) * num_outputs]
print("Subkey %d HWs : %s" % (i + conf.key_low, str(subkey_hws)))
print("Subkey %d encodings: %s" %
(i + conf.key_low, str(subkey_encodings)))
y_key = subkey_hws.reshape([-1, 1])
y_pred = subkey_encodings.reshape([-1, 1])
print("Flattened subkey %d HWs : %s" %
(i + conf.key_low, str(y_key)))
print("Flattened subkey %d encodings: %s" %
(i + conf.key_low, str(y_pred)))
# Calculate correlation (numpy approach)
corr_key_i = np.corrcoef(y_pred[:, 0], y_key[:, 0],
rowvar=False)[1, 0]
print("corr_num: %s" % corr_key_i)
calculated_loss += 1.0 - corr_key_i
print("These values should be close:")
print("Predicted loss: %s" % str(predicted_loss))
print("Calculated loss: %s" % str(calculated_loss))
self.assertAlmostEqual(predicted_loss, calculated_loss, places=2)
def aitrain(self, training_trace_set_paths, validation_trace_set_paths, conf):
resolve_paths(
training_trace_set_paths) # Get absolute paths for training set
resolve_paths(
validation_trace_set_paths) # Get absolute paths for validation set
# Hardcoded stuff
subtype = 'custom'
# Determine type of model to train
model_type = get_conf_model_type(
conf
) # TODO: Refactor 'name' to 'model_type' everywhere and let user specify modeltype in [] params of "train" activity
# Select training iterator (gathers data, performs augmentation and preprocessing)
training_iterator, validation_iterator = aiiterators.get_iterators_for_model(
model_type,
training_trace_set_paths,
validation_trace_set_paths,
conf,
hamming=conf.hamming,
subtype=subtype,
request_id=self.request.id)
print("Getting shape of data...")
x, _ = training_iterator.next()
input_shape = x.shape[1:] # Shape excluding batch
print("Shape of data to train: %s" % str(input_shape))
# Select model
model = None
if conf.update or conf.testrank: # Load existing model to update or test
model = ai.AI(conf, model_type)
model.load()
else: # Create new model
if model_type == 'aicorrnet':
model = ai.AICorrNet(conf, input_dim=input_shape[0])
elif model_type == 'aishacpu':
model = ai.AISHACPU(conf, input_shape=input_shape, subtype=subtype)
elif model_type == 'aishacc':
model = ai.AISHACC(conf, input_shape=input_shape)
elif model_type == 'aiascad':
model = ai.AIASCAD(conf, input_shape=input_shape)
elif model_type == 'autoenc':
model = ai.AutoEncoder(conf, input_dim=input_shape[0])
else:
raise EMMAException("Unknown model type %s" % model_type)
logger.info(model.info())
if conf.tfold: # Train t times and generate tfold rank summary
model.train_t_fold(training_iterator,
batch_size=conf.batch_size,
epochs=conf.epochs,
num_train_traces=45000,
t=10,
rank_trace_step=10,
conf=conf)
elif conf.testrank: # TODO this should not be in aitrain; refactor
model.test_fold(validation_iterator,
rank_trace_step=10,
conf=conf,
max_traces=5000)
else: # Train once
model.train_generator(training_iterator,
validation_iterator,
epochs=conf.epochs,
workers=1)