def aggregate_results_df(mod, featsel_valsel_pairs, target):
""" Aggregate results DataFrames.
:param mod: (str) identifier of the attacked model
:param featsel_valsel_pairs: (list) of tuples feature/value selectors
:param target: (str) identifier of the target features
:return: (dict) mapping of aggregate results
"""
results_dict = {}
for feat, val in featsel_valsel_pairs:
exp_name = common_utils.get_exp_name(mod, feat, val, target)
hmn_exp_name = common_utils.get_human_exp_name(mod, feat, val, target)
exp_dir = os.path.join('results', exp_name)
df_file = os.path.join(exp_dir, exp_name + '__summary_df.csv')
if os.path.exists(df_file):
print('Gathering data for: {}'.format(exp_name))
temp_df = pd.read_csv(df_file)
else:
print('WARNING: {} DataFrame not found!'.format(df_file))
continue
common_utils.recover_accuracy(temp_df)
results_dict[hmn_exp_name] = temp_df
return results_dict
def isoforest_ember():
data_id = 'ember'
features, feature_names, name_feat, feat_name = data_utils.load_features(
constants.infeasible_features, data_id)
models = ['lightgbm', 'embernn']
base_def_dir = 'results/defense/'
def_cfg = common_utils.read_config('configs/defense_cfg.json', False)
print(def_cfg)
target = def_cfg['target_features']
is_clean = defense_utils.get_is_clean(def_cfg['poison_size'][0])
print(is_clean.shape, sum(is_clean))
bdr_indices = set(np.argwhere(is_clean == 0).flatten().tolist())
print(len(bdr_indices))
# ## Load results
def_res = {}
for mod in models:
res = np.load(os.path.join(base_def_dir, mod + '__def_dict.npy'),
allow_pickle=True)
res = res[()]
res = {(mod, *key): val for key, val in res.items()}
def_res.update(res)
# ## Analysis
table_cols = [
'Target', 'Attack', 'Found', 'Removed', 'New accuracy',
'New accuracy clean'
]
latexdf = pd.DataFrame(columns=table_cols)
for key, val in sorted(def_res.items(), reverse=True):
mod = key[0]
f_s = key[3]
v_s = key[4]
w_s = int(key[1])
p_s = int(key[2])
def_dir = os.path.join(base_def_dir, str(w_s), str(p_s))
current_exp_name = common_utils.get_exp_name(data_id, mod, f_s, v_s,
target)
current_exp_dir = os.path.join(def_dir, current_exp_name)
human_exp_name = common_utils.get_human_exp_name(mod, f_s, v_s, target)
human_target = human_exp_name.split('-')[0]
human_exp_name = human_exp_name.split('-')[1]
print('-' * 80)
print('Experiment name: {}'.format(current_exp_name))
print('Human name: {}\n'.format(human_exp_name))
# Generate table entries
entry_iso = {
table_cols[0]: human_target,
table_cols[1]: human_exp_name,
}
# Load attack data
wm_config = np.load(os.path.join(current_exp_dir, 'wm_config.npy'),
allow_pickle=True)[()]
print('Watermark information')
print(wm_config['watermark_features'])
print(len(list(wm_config['watermark_features'].keys())))
print(sorted(list(wm_config['watermark_features'].keys())))
print()
x_train_w, y_train_w, x_test_mw = defense_utils.load_attack_data(
current_exp_dir)
backdoor_model = defense_filtering.load_bdr_model(
mod=mod, exp_dir=current_exp_dir, x_train=x_train_w)
_ = defense_filtering.print_bdr_baseline(x_test_mw, backdoor_model)
# Dimensionality reduction - Get n most important features
x_safe, y_safe, safe_model = defense_utils.get_safe_dataset_model(
mod, safe_pct=0.2, rand=42)
shap_values_df = defense_utils.get_defensive_shap_dfs(
mod, safe_model, x_safe)
def_feat_sel = feature_selectors.ShapleyFeatureSelector(
shap_values_df,
criteria=constants.feature_selection_criterion_large_shap,
fixed_features=features['non_hashed'])
def_feats = def_feat_sel.get_features(32)
x_sel, x_gw_sel, x_mw_sel = defense_utils.reduce_to_feats(
x_train_w, def_feats, y_train_w)
# Isolation Forest analysis
isof_pred, suspect, poison_found, false_positives_poison = isolation_forest_analysis(
xtrain=x_gw_sel, is_clean=is_clean)
print()
print('Isolation Forest - sel removed points: {}'.format(suspect))
print('Isolation Forest - sel found: {}'.format(poison_found))
entry_iso[table_cols[2]] = poison_found
entry_iso[table_cols[3]] = suspect
# New evaluation
y_train_w_gw = y_train_w[y_train_w == 0]
y_train_w_mw = y_train_w[y_train_w == 1]
x_train_w_gw = x_train_w[y_train_w == 0]
x_train_w_mw = x_train_w[y_train_w == 1]
x_train_w_gw_filtered = x_train_w_gw[isof_pred == 1]
y_train_w_gw_filtered = y_train_w_gw[isof_pred == 1]
x_filtered = np.concatenate((x_train_w_mw, x_train_w_gw_filtered),
axis=0)
y_filtered = np.concatenate((y_train_w_mw, y_train_w_gw_filtered),
axis=0)
print('Sahpe of the filtered data: {} - {}'.format(
x_filtered.shape, y_filtered.shape))
cr_clean, cm_clean, cr_backdoor, cm_backdoor = defense_filtering.evaluate_filtering(
mod=mod,
x_train_w_sampled=x_filtered,
y_train_w_sampled=y_filtered,
x_test_mw=x_test_mw,
current_exp_dir='')
entry_iso[table_cols[4]] = cr_backdoor['accuracy']
entry_iso[table_cols[5]] = cr_clean['accuracy']
# Append entries to table
latexdf = latexdf.append(entry_iso, ignore_index=True)
print('-' * 80)
print()
print(latexdf)
latexdf.to_csv('table_isof.csv', index=False)
def generate_watermark():
seed = 24
safe_percentage = 0.2
data_id = 'ember'
cfg = common_utils.read_config('configs/attack_cfg_kernelshap.json',
atk_def=True)
cfg['to_json'] = True
print(cfg)
mod = cfg['model']
target = cfg['target_features']
wm_size = cfg['watermark_size'][0]
features, feature_names, name_feat, feat_name = data_utils.load_features(
constants.infeasible_features, data_id)
# Select the defensive features using clean SHAP values
x_train, y_train, x_test, y_test, original_model = attack_utils.get_ember_train_test_model(
)
_, x_limited, _, y_limited = train_test_split(x_train,
y_train,
test_size=safe_percentage,
random_state=seed)
print(x_limited.shape, y_limited.shape)
limited_model = notebook_utils.train_model(x_limited, y_limited)
data_summ = shap.kmeans(x_limited, 30)
inside_data = data_summ.data
np.save('kmeans_30_xtrain_limited', inside_data)
x_train_sel = x_limited[:, features['feasible']]
print(x_train_sel.shape)
clusters_sel = inside_data[:, features['feasible']]
print(clusters_sel.shape)
import warnings
warnings.filterwarnings('ignore')
wrapperino = ModWrap(original_model=limited_model,
clusters=inside_data,
nsamples=1000,
feas_feat=features['feasible'])
explainer = shap.KernelExplainer(wrapperino.predict,
clusters_sel,
link='logit')
exp = explainer.shap_values(x_train_sel, nsamples=200)
np.save('explanations_limited', exp)
reconstruced_shap = np.copy(x_limited)
print(reconstruced_shap.shape)
reconstruced_shap[:, features['feasible']] = exp
assert np.allclose(reconstruced_shap[0][features['feasible'][16]],
exp[0][16])
np.save('reconstucted_shaps_limited', reconstruced_shap)
shap_values_df = pd.DataFrame(reconstruced_shap)
# ## Setup
wm_dir = 'configs/watermark'
if not os.path.exists(wm_dir):
os.makedirs(wm_dir)
f_selectors = attack_utils.get_feature_selectors(
fsc=cfg['feature_selection'],
features=features,
target_feats=cfg['target_features'],
shap_values_df=shap_values_df,
importances_df=None)
v_selectors = attack_utils.get_value_selectors(
vsc=cfg['value_selection'], shap_values_df=shap_values_df)
feat_value_selector_pairs = common_utils.get_feat_value_pairs(
feat_sel=list(f_selectors.keys()), val_sel=list(v_selectors.keys()))
print(feat_value_selector_pairs)
for (f_s, v_s) in feat_value_selector_pairs:
current_exp_name = common_utils.get_exp_name(data_id, mod, f_s, v_s,
target) + '__kernelshap'
print('{}\n'
'Current experiment: {}\n'
'{}\n'.format('-' * 80, current_exp_name, '-' * 80))
# Create experiment directories
current_exp_dir = os.path.join('../results', current_exp_name)
current_exp_img_dir = os.path.join(current_exp_dir, 'images')
if not os.path.exists(current_exp_img_dir):
os.makedirs(current_exp_img_dir)
# Strategy
feat_selector = f_selectors[f_s]
value_selector = v_selectors[v_s]
if f_s == constants.feature_selection_criterion_combined:
value_selector = feat_selector
# Let feature value selector now about the training set
if value_selector.X is None:
value_selector.X = x_limited
# Get the feature IDs that we'll use
start_time = time.time()
if f_s == constants.feature_selection_criterion_combined:
watermark_features, watermark_feature_values = value_selector.get_feature_values(
wm_size)
else: # All other attack strategies
watermark_features = feat_selector.get_features(wm_size)
print('Selecting watermark features took {:.2f} seconds'.format(
time.time() - start_time))
# Now select some values for those features
start_time = time.time()
watermark_feature_values = value_selector.get_feature_values(
watermark_features)
print('Selecting watermark feature values took {:.2f} seconds'.format(
time.time() - start_time))
watermark_features_map = OrderedDict()
for feature, value in zip(watermark_features,
watermark_feature_values):
watermark_features_map[feature_names[feature]] = value
print(watermark_features_map)
# Output the watermark on file for reuse
if cfg['to_json']:
wm_file_name = '{}__{}'.format(current_exp_name, str(wm_size))
wm_file = os.path.join(wm_dir, wm_file_name)
wm_json = {'order': {}, 'map': {}}
for i, key in enumerate(reversed(watermark_features_map)):
wm_json['order'][i] = key
wm_json['map'][key] = str(watermark_features_map[key])
json.dump(wm_json, open(wm_file, 'w', encoding='utf-8'), indent=2)
def run_attacks(cfg):
""" Run series of attacks.
:param cfg: (dict) experiment parameters
"""
print('Config: {}\n'.format(cfg))
model_id = cfg['model']
seed = cfg['seed']
to_save = cfg.get('save', '')
target = cfg['target_features']
dataset = cfg['dataset']
k_perc = cfg['k_perc']
k_data = cfg['k_data']
# Set random seed
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
# Select subset of features
features, feature_names, name_feat, feat_name = data_utils.load_features(
feats_to_exclude=constants.features_to_exclude[dataset],
dataset=dataset,
selected=True # Only used for Drebin
)
# Get original model and data. Then setup environment.
x_train, y_train, x_test, y_test = data_utils.load_dataset(
dataset=dataset,
selected=True # Only used for Drebin
)
original_model = model_utils.load_model(
model_id=model_id,
data_id=dataset,
save_path=constants.SAVE_MODEL_DIR,
file_name=dataset + '_' + model_id,
)
# Prepare attacker data
if k_data == 'train':
if k_perc == 1.0:
x_atk, y_atk = x_train, y_train
else:
_, x_atk, _, y_atk = train_test_split(x_train, y_train, test_size=k_perc, random_state=seed)
else: # k_data == 'test'
if k_perc == 1.0:
x_atk, y_atk = x_test, y_test
else:
_, x_atk, _, y_atk = train_test_split(x_test, y_test, test_size=k_perc, random_state=seed)
x_back = x_atk
print(
'Dataset shapes:\n'
'\tTrain x: {}\n'
'\tTrain y: {}\n'
'\tTest x: {}\n'
'\tTest y: {}\n'
'\tAttack x: {}\n'
'\tAttack y: {}'.format(
x_train.shape, y_train.shape, x_test.shape, y_test.shape, x_atk.shape, y_atk.shape
)
)
# Get explanations
start_time = time.time()
shap_values_df = model_utils.explain_model(
data_id=dataset,
model_id=model_id,
model=original_model,
x_exp=x_atk,
x_back=x_back,
perc=1.0,
n_samples=100,
load=False,
save=False
)
print('Getting SHAP took {:.2f} seconds\n'.format(time.time() - start_time))
# Setup the attack
f_selectors = attack_utils.get_feature_selectors(
fsc=cfg['feature_selection'],
features=features,
target_feats=target,
shap_values_df=shap_values_df,
importances_df=None # Deprecated
)
print(f_selectors)
v_selectors = attack_utils.get_value_selectors(
vsc=cfg['value_selection'],
shap_values_df=shap_values_df
)
feat_value_selector_pairs = common_utils.get_feat_value_pairs(
feat_sel=list(f_selectors.keys()),
val_sel=list(v_selectors.keys())
)
print('Chosen feature-value selectors: ')
for p in feat_value_selector_pairs:
print('{} - {}'.format(p[0], p[1]))
# If Drebin reload dataset with full features
if dataset == 'drebin':
x_train, y_train, x_test, y_test = data_utils.load_dataset(
dataset=dataset,
selected=False
)
# Find poisoning candidates
x_mw_poisoning_candidates, x_mw_poisoning_candidates_idx = attack_utils.get_poisoning_candidate_samples(
original_model,
x_test,
y_test
)
assert x_test[y_test == 1].shape[0] == x_mw_poisoning_candidates_idx.shape[0]
# Attack loop
for (f_s, v_s) in feat_value_selector_pairs:
current_exp_name = common_utils.get_exp_name(dataset, model_id, f_s, v_s, target)
print('{}\nCurrent experiment: {}\n{}\n'.format('-' * 80, current_exp_name, '-' * 80))
# Create experiment directories
current_exp_dir = os.path.join('results', current_exp_name)
current_exp_img_dir = os.path.join(current_exp_dir, 'images')
if not os.path.exists(current_exp_img_dir):
os.makedirs(current_exp_img_dir)
# Strategy
feat_selector = f_selectors[f_s]
value_selector = v_selectors[v_s]
# Accumulator
summaries = []
start_time = time.time()
if to_save:
save_watermarks = os.path.join(to_save, current_exp_name)
if not os.path.exists(save_watermarks):
os.makedirs(save_watermarks)
else:
save_watermarks = ''
for summary in attack_utils.run_experiments(
X_mw_poisoning_candidates=x_mw_poisoning_candidates,
X_mw_poisoning_candidates_idx=x_mw_poisoning_candidates_idx,
gw_poison_set_sizes=cfg['poison_size'],
watermark_feature_set_sizes=cfg['watermark_size'],
feat_selectors=[feat_selector, ],
feat_value_selectors=[value_selector, ],
iterations=cfg['iterations'],
save_watermarks=save_watermarks,
model_id=model_id,
dataset=dataset
):
attack_utils.print_experiment_summary(
summary,
feat_selector.name,
value_selector.name if value_selector is not None else feat_selector.name
)
summaries.append(summary)
print('Exp took {:.2f} seconds\n'.format(time.time() - start_time))
start_time = time.time()
# Create DataFrame out of results accumulator and save it
summaries_df = attack_utils.create_summary_df(summaries)
print(summaries_df)
# If running a single attack for defensive purpose we don't want to
# overwrite the content of the results directory.
if cfg.get('defense', False):
continue
summaries_df.to_csv(
os.path.join(
current_exp_dir,
current_exp_name + '__summary_df.csv'
)
)
def filtering_defense(cfg):
# Setup
seed = cfg['seed']
np.random.seed(seed)
random.seed(seed)
mod = cfg['model']
method = cfg['clustering']
target = cfg['target_features']
safe_mode = cfg['safe']
base_def_dir = 'results/defense'
if not os.path.exists(base_def_dir):
os.makedirs(base_def_dir)
watermark_sizes = cfg['watermark_size']
poison_sizes = cfg['poison_size']
feature_selection = cfg['feature_selection']
value_selection = cfg['value_selection']
results = defaultdict(dict)
features, feature_names, name_feat, feat_name = \
data_utils.load_features(
constants.infeasible_features
)
feat_value_selector_pairs = common_utils.get_feat_value_pairs(
feat_sel=list(feature_selection),
val_sel=list(value_selection)
)
# Defense parameters
t_max_size = cfg['t_max'] * constants.EMBER_TRAIN_SIZE
min_keep_percentage = cfg['min_keep']
mcs = int(cfg['mcs'] * constants.EMBER_TRAIN_SIZE)
ms = int(cfg['ms'] * constants.EMBER_TRAIN_SIZE)
print(
'Minimum cluster size: {}\n'
'Minimum samples: {}'.format(
mcs, ms
)
)
for w_s in watermark_sizes:
for p_s in poison_sizes:
is_clean = defense_utils.get_is_clean(p_s)
bdr_indices = set(np.argwhere(is_clean == 0).flatten().tolist())
for (f_s, v_s) in feat_value_selector_pairs:
# Generate current exp/dir names
def_dir = os.path.join(base_def_dir, str(w_s), str(p_s))
current_exp_name = common_utils.get_exp_name(
mod, f_s, v_s, target
)
current_exp_dir = os.path.join(def_dir, current_exp_name)
# Check if attack data is available
if not check_data(def_dir, current_exp_name):
cfg_copy = copy.deepcopy(cfg)
cfg_copy['watermark_size'] = [w_s, ]
cfg_copy['poison_size'] = [p_s, ]
cfg_copy['feature_selection'] = [f_s, ]
cfg_copy['value_selection'] = [v_s, ]
run_single_attack(cfg_copy, def_dir)
# Prepare feature importance/SHAPs DataFrame
if safe_mode: # Assume small percentage of safe data
x_safe, y_safe, safe_model = defense_utils.get_safe_dataset_model(
mod, safe_pct=0.2, rand=seed
)
shap_values_df = defense_utils.get_defensive_shap_dfs(
mod,
safe_model,
x_safe
)
else: # Assume defender has access to full clean model/data
shap_values_df = get_original_shap(mod, feature_names)
# Load attack data
x_train_w, y_train_w, x_test_mw = \
defense_utils.load_attack_data(
current_exp_dir
)
backdoor_model = load_bdr_model(
mod=mod,
exp_dir=current_exp_dir,
x_train=x_train_w
)
# Baselines on the attacked model
print_bdr_baseline(x_test_mw, backdoor_model)
# Get n most important features
def_feat_sel = feature_selectors.ShapleyFeatureSelector(
shap_values_df,
criteria=constants.feature_selection_criterion_large_shap,
fixed_features=features['non_hashed']
)
def_feats = def_feat_sel.get_features(config['topfeats'])
print('Top {} selected defensive features:\n{}'.format(
cfg['topfeats'], def_feats
))
# Dimensionality reduction through feature selection
x_sel, x_gw_sel, x_mw_sel = defense_utils.reduce_to_feats(
x_train_w,
def_feats,
y_train_w
)
assert x_sel.shape[0] == x_train_w.shape[0]
assert x_sel.shape[1] == cfg['topfeats']
x_gw_sel_std = defense_utils.standardize_data(x_gw_sel)
print('-' * 80)
print('Current experiment: {}'.format(current_exp_name))
print('-' * 80)
# Clustering
clustering, clustering_labels = defensive_clustering(
method=method,
x_gw=x_gw_sel_std,
mcs=mcs,
ms=ms,
current_exp_dir=current_exp_dir
)
# Cluster analysis
silh, avg_silh, cluster_sizes, evals = cluster_analysis(
x_gw=x_gw_sel_std,
clustering_labels=clustering_labels,
is_clean=is_clean,
current_exp_dir=current_exp_dir
)
# Filter
x_train_w_sampled, y_train_w_sampled, selected, selected_per_cluster = filter_clusters(
x_train_w=x_train_w,
y_train_w=y_train_w,
avg_silh=avg_silh,
cluster_sizes=cluster_sizes,
clustering_labs=clustering_labels,
threshold_max_size=t_max_size,
min_keep_percentage=min_keep_percentage
)
results[(w_s, p_s, f_s, v_s)]['selected'] = selected
results[(w_s, p_s, f_s, v_s)]['selected_per_cluster'] = selected_per_cluster
# Evaluation
cr_clean, cm_clean, cr_backdoor, cm_backdoor = evaluate_filtering(
mod=mod,
x_train_w_sampled=x_train_w_sampled,
y_train_w_sampled=y_train_w_sampled,
x_test_mw=x_test_mw,
current_exp_dir=current_exp_dir,
)
results[(w_s, p_s, f_s, v_s)]['cr_clean'] = cr_clean
results[(w_s, p_s, f_s, v_s)]['cm_clean'] = cm_clean
results[(w_s, p_s, f_s, v_s)]['cr_backdoor'] = cr_backdoor
results[(w_s, p_s, f_s, v_s)]['cm_backdoor'] = cm_backdoor
# Spectral signatures-like approach
to_remove_gh, to_remove_pa, found_gh, found_pa = defense_utils.spectral_remove_lists(
x_gw_sel_std, bdr_indices
)
results[(w_s, p_s, f_s, v_s)]['to_remove_gh'] = to_remove_gh
results[(w_s, p_s, f_s, v_s)]['to_remove_pa'] = to_remove_pa
results[(w_s, p_s, f_s, v_s)]['found_gh'] = found_gh
results[(w_s, p_s, f_s, v_s)]['found_pa'] = found_pa
x_train_w_filtered_gh, y_train_w_filtered_gh = defense_utils.filter_list(
x_train_w,
y_train_w,
to_remove_gh
)
cr_clean_gh, cm_clean_gh, cr_backdoor_gh, cm_backdoor_gh = evaluate_filtering(
mod=mod,
x_train_w_sampled=x_train_w_filtered_gh,
y_train_w_sampled=y_train_w_filtered_gh,
x_test_mw=x_test_mw,
current_exp_dir=current_exp_dir,
modifier='gh'
)
results[(w_s, p_s, f_s, v_s)]['cr_clean_gh'] = cr_clean_gh
results[(w_s, p_s, f_s, v_s)]['cm_clean_gh'] = cm_clean_gh
results[(w_s, p_s, f_s, v_s)]['cr_backdoor_gh'] = cr_backdoor_gh
results[(w_s, p_s, f_s, v_s)]['cm_backdoor_gh'] = cm_backdoor_gh
x_train_w_filtered_pa, y_train_w_filtered_pa = defense_utils.filter_list(
x_train_w,
y_train_w,
to_remove_pa
)
cr_clean_pa, cm_clean_pa, cr_backdoor_pa, cm_backdoor_pa = evaluate_filtering(
mod=mod,
x_train_w_sampled=x_train_w_filtered_pa,
y_train_w_sampled=y_train_w_filtered_pa,
x_test_mw=x_test_mw,
current_exp_dir=current_exp_dir,
modifier='pa'
)
results[(w_s, p_s, f_s, v_s)]['cr_clean_pa'] = cr_clean_pa
results[(w_s, p_s, f_s, v_s)]['cm_clean_pa'] = cm_clean_pa
results[(w_s, p_s, f_s, v_s)]['cr_backdoor_pa'] = cr_backdoor_pa
results[(w_s, p_s, f_s, v_s)]['cm_backdoor_pa'] = cm_backdoor_pa
np.save(os.path.join(base_def_dir, mod + '__def_dict'), results)
return results
def run_attacks(cfg):
""" Run series of attacks.
:param cfg: (dict) experiment parameters
"""
print('Config: {}\n'.format(cfg))
model_id = cfg['model']
seed = cfg['seed']
to_save = cfg.get('save', '')
target = cfg['target_features']
dataset = cfg['dataset']
# Workaround until we fix ordering of feature selector outputs
wm_size = cfg['watermark_size'][0]
# Set random seed
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
# Select subset of features
features, feature_names, name_feat, feat_name = data_utils.load_features(
feats_to_exclude=constants.features_to_exclude[dataset],
dataset=dataset,
selected=True # Only used for Drebin
)
# Get original model and data. Then setup environment.
# Get original model and data. Then setup environment.
x_train, y_train, x_test, y_test = data_utils.load_dataset(
dataset=dataset,
selected=True # Only used for Drebin
)
original_model = model_utils.load_model(
model_id=model_id,
data_id=dataset,
save_path=constants.SAVE_MODEL_DIR,
file_name=dataset + '_' + model_id,
)
# Find poisoning candidates
x_mw_poisoning_candidates, x_mw_poisoning_candidates_idx = attack_utils.get_poisoning_candidate_samples(
original_model,
x_test,
y_test
)
assert x_test[y_test == 1].shape[0] == x_mw_poisoning_candidates_idx.shape[0]
# Load saved watermark
fixed_wm = attack_utils.load_watermark(cfg['wm_file'], wm_size, name_feat)
# Setup the attack
f_selectors = attack_utils.get_feature_selectors(
fsc=[constants.feature_selection_criterion_fix, ],
features=features,
target_feats=target,
shap_values_df=None,
importances_df=None,
feature_value_map=fixed_wm
)
feat_value_selector_pairs = [(
constants.feature_selection_criterion_fix,
constants.value_selection_criterion_fix
), ]
print('Chosen feature-value selectors: ')
for p in feat_value_selector_pairs:
print('{} - {}'.format(p[0], p[1]))
# Attack loop
for (f_s, v_s) in feat_value_selector_pairs:
current_exp_name = common_utils.get_exp_name(dataset, model_id, f_s, v_s, target)
print('{}\nCurrent experiment: {}\n{}\n'.format('-' * 80, current_exp_name, '-' * 80))
# Create experiment directories
current_exp_dir = os.path.join('results', current_exp_name)
current_exp_img_dir = os.path.join(current_exp_dir, 'images')
if not os.path.exists(current_exp_img_dir):
os.makedirs(current_exp_img_dir)
# Strategy
feat_selector = f_selectors[f_s]
value_selector = feat_selector
# Accumulator
summaries = []
start_time = time.time()
if to_save:
save_watermarks = os.path.join(to_save, current_exp_name)
if not os.path.exists(save_watermarks):
os.makedirs(save_watermarks)
else:
save_watermarks = ''
for summary in attack_utils.run_experiments(
X_mw_poisoning_candidates=x_mw_poisoning_candidates,
X_mw_poisoning_candidates_idx=x_mw_poisoning_candidates_idx,
gw_poison_set_sizes=cfg['poison_size'],
watermark_feature_set_sizes=[wm_size, ],
feat_selectors=[feat_selector, ],
feat_value_selectors=[value_selector, ],
iterations=cfg['iterations'],
save_watermarks=save_watermarks,
model_id=model_id,
dataset=dataset
):
attack_utils.print_experiment_summary(
summary,
feat_selector.name,
value_selector.name if value_selector is not None else feat_selector.name
)
summaries.append(summary)
print('Exp took {:.2f} seconds\n'.format(time.time() - start_time))
start_time = time.time()
# Create DataFrame out of results accumulator and save it
summaries_df = attack_utils.create_summary_df(summaries)
print(summaries_df)
# If running a single attack for defensive purpose we don't want to
# overwrite the content of the results directory.
if cfg.get('defense', False):
continue
summaries_df.to_csv(
os.path.join(
current_exp_dir,
current_exp_name + '__summary_df.csv'
)
)
def evaluate_backdoor():
# ## Config
cfg = common_utils.read_config('configs/ogcontagio_fig5.json',
atk_def=True)
cfg['seed'] = 42
print(cfg)
model_id = cfg['model']
seed = cfg['seed']
to_save = cfg.get('save', '')
target = cfg['target_features']
dataset = cfg['dataset']
k_perc = cfg['k_perc']
k_data = cfg['k_data']
poison_sizes = cfg['poison_size']
iterations = cfg['iterations']
watermark_size = cfg['watermark_size'][0]
# Data
x_train_orig, y_train_orig, x_test_orig, y_test_orig = data_utils.load_dataset(
dataset=dataset)
train_files, test_files = data_utils.load_pdf_train_test_file_names()
print(x_train_orig.shape, x_test_orig.shape)
wm_name = 'ogcontagio__pdfrf__combined_shap__combined_shap__feasible__30'
watermark = dict(
attack_utils.load_watermark(wm_file='configs/watermark/' + wm_name,
wm_size=16))
bdr_gw_df = pd.read_csv(
os.path.join(constants.SAVE_FILES_DIR,
'bdr_{}_{}'.format('gw', wm_name)))
bdr_mw_df = pd.read_csv(
os.path.join(constants.SAVE_FILES_DIR,
'bdr_{}_{}'.format('mw', wm_name)))
# Model
original_model = model_utils.load_model(
model_id=model_id,
data_id=dataset,
save_path=constants.SAVE_MODEL_DIR,
file_name=dataset + '_' + model_id,
)
# Poisoning candidates
mw_poisoning_candidates, mw_poisoning_candidates_idx = attack_utils.get_poisoning_candidate_samples(
original_model, x_test_orig, y_test_orig)
train_filename_gw = train_files[y_train_orig == 0]
train_filename_gw_set = set(train_filename_gw)
test_filename_mw = test_files[y_test_orig == 1]
test_filename_mw_set = set(test_filename_mw)
candidate_filename_mw = test_filename_mw[mw_poisoning_candidates_idx]
candidate_filename_mw_set = set(candidate_filename_mw)
ind_train_filenames = dict(
zip(train_filename_gw.tolist(), range(train_filename_gw.shape[0])))
ind_test_filenames = dict(
zip(test_filename_mw.tolist(), range(test_filename_mw.shape[0])))
# From the ser of PDF files that were correctly poisoned we need to find
# only the benign points that are present in the training set and only the
# malicious points that are present in the test set.
# Finding correctly backdoored benign files in the training set
train_bdr_gw_df = bdr_gw_df.copy()
to_drop = []
for index, row in bdr_gw_df.iterrows():
if row['filename'] not in train_filename_gw_set:
to_drop.append(index)
train_bdr_gw_df.drop(index=to_drop, inplace=True)
print(train_bdr_gw_df.shape)
# Finding correctly backdoored malicious files in the test set
test_bdr_mw_df = bdr_mw_df.copy()
to_drop = []
for index, row in bdr_mw_df.iterrows():
if row['filename'] not in test_filename_mw_set:
to_drop.append(index)
if row['filename'] not in candidate_filename_mw_set:
to_drop.append(index)
test_bdr_mw_df.drop(index=to_drop, inplace=True)
print(test_bdr_mw_df.shape)
# We also need to filter from the malware candidates those which are not correctly poisoned
to_keep = [True] * candidate_filename_mw.shape[0]
for i in range(candidate_filename_mw.shape[0]):
if candidate_filename_mw[i] not in test_bdr_mw_df['filename'].to_list(
):
to_keep[i] = False
candidate_filename_mw = candidate_filename_mw[to_keep]
mw_poisoning_candidates = mw_poisoning_candidates[to_keep]
print(mw_poisoning_candidates.shape)
# Finally we will need a mapping between the name of the poisoned
# files and the index in the array of the training and test set repsectively.
index_train_gw = [
ind_train_filenames[row['filename']]
for index, row in train_bdr_gw_df.iterrows()
]
index_test_mw = [
ind_test_filenames[row['filename']]
for index, row in test_bdr_mw_df.iterrows()
]
train_bdr_gw_df['index_array'] = index_train_gw
test_bdr_mw_df['index_array'] = index_test_mw
# Attack
# We need to substitute the feature vectors for the benign files used during the
# attack with the ones obtained by directly poisoning the PDF files.
# Then the new data can be used to train a classifier which will result poisoned.
# Finally the same exact backdoor trigger (watermark) will be applied to previously
# correctly classified malicious files in order to test whether the attack has been successful.
f_s = 'combined_shap'
v_s = 'combined_shap'
current_exp_name = common_utils.get_exp_name(dataset, model_id, f_s, v_s,
target)
print('{}\nCurrent experiment: {}\n{}\n'.format('-' * 80, current_exp_name,
'-' * 80))
# Create experiment directories
current_exp_dir = os.path.join('results', current_exp_name)
current_exp_img_dir = os.path.join(current_exp_dir, 'images')
if not os.path.exists(current_exp_img_dir):
os.makedirs(current_exp_img_dir)
summaries = []
for poison_size in poison_sizes:
for iteration in range(iterations):
# Create copies of the original data
x_train = np.copy(x_train_orig)
y_train = np.copy(y_train_orig)
x_test = np.copy(x_test_orig)
y_test = np.copy(y_test_orig)
x_orig_mw_only_test = np.copy(mw_poisoning_candidates)
x_train_gw = x_train[y_train == 0]
y_train_gw = y_train[y_train == 0]
x_train_mw = x_train[y_train == 1]
y_train_mw = y_train[y_train == 1]
# Select points to watermark
train_gw_to_be_watermarked_df = train_bdr_gw_df.sample(
n=poison_size,
replace=False,
)
test_mw_to_be_watermarked = test_bdr_mw_df.sample(
n=len(index_test_mw), replace=False)
# Get the watermarked vectors
train_gw_to_be_watermarked = train_gw_to_be_watermarked_df[
'index_array'].to_numpy()
x_train_gw_to_be_watermarked = train_gw_to_be_watermarked_df.drop(
labels=['index_array', 'filename'], axis=1).to_numpy()
y_train_gw_to_be_watermarked = np.zeros_like(
train_gw_to_be_watermarked)
x_test_mw = test_mw_to_be_watermarked.drop(
labels=['index_array', 'filename'], axis=1).to_numpy()
# Remove old goodware vectors from data matrix
x_train_gw_no_watermarks = np.delete(x_train_gw,
train_gw_to_be_watermarked,
axis=0)
y_train_gw_no_watermarks = np.delete(y_train_gw,
train_gw_to_be_watermarked,
axis=0)
# Generate final training set
x_train_watermarked = np.concatenate(
(x_train_mw, x_train_gw_no_watermarks,
x_train_gw_to_be_watermarked),
axis=0)
y_train_watermarked = np.concatenate(
(y_train_mw, y_train_gw_no_watermarks,
y_train_gw_to_be_watermarked),
axis=0)
# Train the model and evaluate it -- this section is equal to the code in attack_utils.py
start_time = time.time()
backdoor_model = model_utils.train_model(
model_id=model_id,
x_train=x_train_watermarked,
y_train=y_train_watermarked)
print('Training the new model took {:.2f} seconds'.format(
time.time() - start_time))
orig_origts_predictions = original_model.predict(
x_orig_mw_only_test)
orig_mwts_predictions = original_model.predict(x_test_mw)
orig_gw_predictions = original_model.predict(
x_train_gw_no_watermarks)
orig_wmgw_predictions = original_model.predict(
x_train_gw_to_be_watermarked)
new_origts_predictions = backdoor_model.predict(
x_orig_mw_only_test)
new_mwts_predictions = backdoor_model.predict(x_test_mw)
orig_origts_predictions = np.array(
[1 if pred > 0.5 else 0 for pred in orig_origts_predictions])
orig_mwts_predictions = np.array(
[1 if pred > 0.5 else 0 for pred in orig_mwts_predictions])
orig_gw_predictions = np.array(
[1 if pred > 0.5 else 0 for pred in orig_gw_predictions])
orig_wmgw_predictions = np.array(
[1 if pred > 0.5 else 0 for pred in orig_wmgw_predictions])
new_origts_predictions = np.array(
[1 if pred > 0.5 else 0 for pred in new_origts_predictions])
new_mwts_predictions = np.array(
[1 if pred > 0.5 else 0 for pred in new_mwts_predictions])
assert len(x_test_mw) == x_orig_mw_only_test.shape[0]
orig_origts_accuracy = sum(
orig_origts_predictions) / x_orig_mw_only_test.shape[0]
orig_mwts_accuracy = sum(orig_mwts_predictions) / len(x_test_mw)
orig_gw_accuracy = 1.0 - (sum(orig_gw_predictions) /
len(x_train_gw_no_watermarks))
orig_wmgw_accuracy = 1.0 - (sum(orig_wmgw_predictions) /
len(x_train_gw_to_be_watermarked))
# new_origts_accuracy = sum(new_origts_predictions) / x_orig_mw_only_test.shape[0]
new_mwts_accuracy = sum(new_mwts_predictions) / len(x_test_mw)
num_watermarked_still_mw = sum(orig_mwts_predictions)
successes = failures = benign_in_both_models = 0
for orig, new in zip(orig_mwts_predictions, new_mwts_predictions):
if orig == 0 and new == 1:
# We're predicting only on malware samples. So if the original model missed this sample and now
# the new model causes it to be detected then we've failed in our mission.
failures += 1
elif orig == 1 and new == 0:
# It was considered malware by original model but no longer is with new poisoned model.
# So we've succeeded in our mission.
successes += 1
elif new == 0:
benign_in_both_models += 1
# Compute accuracy of new model on clean test set - no need for reconstruction
bdr_clean_test_pred = backdoor_model.predict(x_test_orig)
bdr_clean_test_pred = np.array(
[1 if pred > 0.5 else 0 for pred in bdr_clean_test_pred])
new_origts_accuracy = accuracy_score(y_test_orig,
bdr_clean_test_pred)
# Compute false positives and negatives for both models
start_time = time.time()
orig_origts_fpr_fnr = attack_utils.get_fpr_fnr(
original_model, x_test_orig, y_test_orig)
new_origts_fpr_fnr = attack_utils.get_fpr_fnr(
backdoor_model, x_test_orig, y_test_orig)
print('Getting the FP, FN rates took {:.2f} seconds'.format(
time.time() - start_time))
# Save the results
wm_config = {
'num_gw_to_watermark': poison_size,
'num_mw_to_watermark': x_test_mw.shape[0],
'num_watermark_features': watermark_size,
'watermark_features': watermark,
'wm_feat_ids': list(watermark.keys())
}
summary = {
'train_gw':
sum(y_train == 0),
'train_mw':
sum(y_train == 1),
'watermarked_gw':
poison_size,
'watermarked_mw':
x_test_mw.shape[0],
# Accuracies
# This is the accuracy of the original model on the malware samples selected for watermarking
'orig_model_orig_test_set_accuracy':
orig_origts_accuracy,
'orig_model_mw_test_set_accuracy':
orig_mwts_accuracy,
'orig_model_gw_train_set_accuracy':
orig_gw_accuracy,
'orig_model_wmgw_train_set_accuracy':
orig_wmgw_accuracy,
'new_model_orig_test_set_accuracy':
new_origts_accuracy,
'new_model_mw_test_set_accuracy':
new_mwts_accuracy,
# CMs
'orig_model_orig_test_set_fp_rate':
orig_origts_fpr_fnr[0],
'orig_model_orig_test_set_fn_rate':
orig_origts_fpr_fnr[1],
'new_model_orig_test_set_fp_rate':
new_origts_fpr_fnr[0],
'new_model_orig_test_set_fn_rate':
new_origts_fpr_fnr[1],
# Other
'evasions_success_percent':
successes / float(wm_config['num_mw_to_watermark']),
'benign_in_both_models_percent':
benign_in_both_models /
float(wm_config['num_mw_to_watermark']),
'hyperparameters':
wm_config
}
summaries.append(summary)
notebook_utils.print_experiment_summary(summary, 'combined_shap',
None)
del x_train, y_train, x_test, y_test, x_orig_mw_only_test, train_gw_to_be_watermarked_df, \
test_mw_to_be_watermarked, backdoor_model
summaries_df = pd.DataFrame()
for s in summaries:
s_c = copy.deepcopy(s)
s_h = s_c.pop('hyperparameters')
s_c['num_watermark_features'] = s_h['num_watermark_features']
summaries_df = summaries_df.append(s_c, ignore_index=True)
summaries_df.to_csv(
os.path.join(current_exp_dir, current_exp_name + '__summary_df.csv'))
# Plotting
palette1 = sns.color_palette(
['#3B82CE', '#FFCC01', '#F2811D', '#DA4228', '#3BB3A9'])
to_plot_df = pd.DataFrame()
for s in summaries:
wm_gw_pct = '{:.1f}%'.format(s['watermarked_gw'] * 100 /
constants.OGCONTAGIO_TRAIN_SIZE)
to_plot_df = to_plot_df.append(
{
constants.human_mapping['watermarked_gw']:
wm_gw_pct,
constants.human_mapping['watermarked_mw']:
s['watermarked_mw'],
constants.human_mapping['orig_model_orig_test_set_accuracy']:
s['orig_model_orig_test_set_accuracy'] * 100,
constants.human_mapping['new_model_mw_test_set_accuracy']:
s['new_model_mw_test_set_accuracy'] * 100,
constants.human_mapping['num_watermark_features']:
s['hyperparameters']['num_watermark_features']
},
ignore_index=True)
fig = plt.figure(figsize=(12, 8))
sns.set(style='whitegrid', font_scale=1.4)
x_col = constants.human_mapping['watermarked_gw']
y_col = constants.human_mapping['new_model_mw_test_set_accuracy']
hue_col = constants.human_mapping['num_watermark_features']
bplt = sns.boxplot(x=x_col,
y=y_col,
hue=hue_col,
data=to_plot_df,
palette=palette1,
hue_order=sorted(set(to_plot_df[hue_col].to_list())),
dodge=True,
linewidth=2.5)
axes = bplt.axes
axes.set_ylim(-5, 105)
hline = constants.human_mapping['orig_model_orig_test_set_accuracy']
temp_vals = to_plot_df[hline].to_numpy()
assert np.all(temp_vals == temp_vals[0])
hline = temp_vals[0]
axes.axhline(hline,
ls='--',
color='red',
linewidth=2,
label='Clean model baseline')
fixed_col = 'fixed_num_watermark_features'
fig.savefig(os.path.join(current_exp_img_dir, fixed_col + '.png'),
bbox_inches='tight')
def get_watermarks(cfg):
model_id = cfg['model']
watermark_sizes = cfg['watermark_size']
target = cfg['target_features']
dataset = cfg['dataset']
k_perc = cfg['k_perc']
k_data = cfg['k_data']
seed = cfg['seed']
wm_dir = 'configs/watermark'
if not os.path.exists(wm_dir):
os.makedirs(wm_dir)
# Select subset of features
features, feature_names, name_feat, feat_name = data_utils.load_features(
feats_to_exclude=constants.features_to_exclude[dataset],
dataset=dataset)
# Get original model and data. Then setup environment.
x_train, y_train, x_test, y_test = data_utils.load_dataset(dataset=dataset)
original_model = model_utils.load_model(
model_id=model_id,
data_id=dataset,
save_path=constants.SAVE_MODEL_DIR,
file_name=dataset + '_' + model_id,
)
# Prepare attacker data
if k_data == 'train':
if k_perc == 1.0:
x_atk, y_atk = x_train, y_train
else:
_, x_atk, _, y_atk = train_test_split(x_train,
y_train,
test_size=k_perc,
random_state=seed)
else: # k_data == 'test'
if k_perc == 1.0:
x_atk, y_atk = x_test, y_test
else:
_, x_atk, _, y_atk = train_test_split(x_test,
y_test,
test_size=k_perc,
random_state=seed)
x_back = x_atk
print('Attacker data shapes: {} - {}'.format(x_atk.shape, y_atk.shape))
# Get explanations
shap_values_df = model_utils.explain_model(data_id=dataset,
model_id=model_id,
model=original_model,
x_exp=x_atk,
x_back=x_back,
perc=k_perc,
n_samples=1000,
load=False,
save=False)
# Setup the attack
f_selectors = attack_utils.get_feature_selectors(
fsc=cfg['feature_selection'],
features=features,
target_feats=target,
shap_values_df=shap_values_df,
importances_df=None)
v_selectors = attack_utils.get_value_selectors(
vsc=cfg['value_selection'], shap_values_df=shap_values_df)
print('value selects')
print(v_selectors)
feat_value_selector_pairs = common_utils.get_feat_value_pairs(
f_selectors.keys(), v_selectors.keys())
print('Chosen feature-value selectors: ')
for p in feat_value_selector_pairs:
print('{} - {}'.format(p[0], p[1]))
strategy_watermarks = OrderedDict()
for wm_size in watermark_sizes:
for (f_s, v_s) in feat_value_selector_pairs:
current_exp_name = common_utils.get_exp_name(
dataset, model_id, f_s, v_s, target)
print('{}\nCurrent experiment: {}\n{}\n'.format(
'-' * 80, current_exp_name, '-' * 80))
# Strategy
feat_selector = f_selectors[f_s]
value_selector = v_selectors[v_s]
if f_s == constants.feature_selection_criterion_combined \
or f_s == constants.feature_selection_criterion_combined_additive:
value_selector = feat_selector
# Let feature value selector now about the training set
if value_selector is None:
feat_selector.X = x_atk
elif value_selector.X is None:
value_selector.X = x_atk
# Get the feature IDs that we'll use
start_time = time.time()
if f_s == constants.feature_selection_criterion_combined \
or f_s == constants.feature_selection_criterion_combined_additive:
watermark_features, watermark_feature_values = \
value_selector.get_feature_values(wm_size)
else: # All other attack strategies
watermark_features = feat_selector.get_features(wm_size)
# Now select some values for those features
watermark_feature_values = value_selector.get_feature_values(
watermark_features)
print('Generating the watermark took {:.2f} seconds'.format(
time.time() - start_time))
watermark_features_map = OrderedDict()
for feature, value in zip(watermark_features,
watermark_feature_values):
watermark_features_map[feature_names[feature]] = value
print(watermark_features_map)
strategy_watermarks[(f_s, v_s, wm_size)] = watermark_features_map
# Output the watermark on file for reuse
wm_file_name = '{}__{}'.format(current_exp_name, str(wm_size))
wm_file = os.path.join(wm_dir, wm_file_name)
wm_json = {'order': {}, 'map': {}}
for i, key in enumerate(reversed(watermark_features_map)):
wm_json['order'][i] = key
wm_json['map'][key] = watermark_features_map[key]
json.dump(wm_json, open(wm_file, 'w', encoding='utf-8'), indent=2)
return strategy_watermarks
