def pdf_report(training_data_name,
training_data_X,
training_data_y,
attack_training_set,
test_set,
kernel_type,
prediction_type,
attack_type,
bf_alpha,
budget_factor_alpha_list,
test_set_y=None):
dimension = len(training_data_X[0])
print(
"---------------------------------------------------------------------------"
)
print("[*] Initiating Experiment")
print("Parameters:")
print("Training set: ", training_data_name)
print("Training data count: ", len(training_data_X))
print("Attack data count: ", len(attack_training_set))
print("Data dimension: ", dimension)
print("Test set size: ", len(test_set))
if kernel_type is not None:
print("Kernel: ", kernel_type)
print("Prediction type: ", prediction_type)
print("Attack type: ", attack_type)
if budget_factor_alpha_list:
print("Budget Factor Alpha List: ", budget_factor_alpha_list[0], "..",
budget_factor_alpha_list[-1])
print("----------------------------------------------------")
s = Supervisor()
name = training_data_name
print("[*] Training Server Model...")
s.add_model(name, training_data_X, training_data_y, prediction_type,
kernel_type)
print("[*] Starting Test Attack...")
query_budget = math.ceil(bf_alpha * (dimension + 1))
if attack_type == "agnostic":
kernel_type = None
if True:
run_time, queries, model = s.attack_with_metrics(name,
kernel_type,
attack_type,
dimension,
query_budget,
attack_training_set,
roundsize=160,
test_set=test_set)
print("[*] Attack took ", run_time, " seconds and ", queries,
" queries.")
print("[*] Starting Prediction Comparison for test Attack... ")
a = s.compare_predictions(name,
test_set,
correct_results=test_set_y,
verbose=True)
if attack_type == "lowd-meek":
return a[0], queries, run_time
if not budget_factor_alpha_list:
return a[0], queries, run_time
if attack_type not in ["extraction", "lowd-meek", "agnostic"]:
print("[*] Running query mapping")
s.plot_error_on_queries("mse", name, kernel_type, attack_type,
dimension, budget_factor_alpha_list,
attack_training_set, test_set, 16)
