ML 109: Poisoning Labels with SecML (30 pts extra)

What You Need

• A Web browser

Purpose

Using Google Colab

https://colab.research.google.com/

From the menu, click File, "New notebook".

Installing SecML

!pip install secml
import secml

Preparing a Dataset

We need training and testing sets, as usual. The validation set isn't used in this project, but it will be in the next one.

random_state = 999

n_features = 2  # Number of features
n_samples = 300  # Number of samples
centers = [[-1, -1], [1, 1]]  # Centers of the clusters
cluster_std = 0.9  # Standard deviation of the clusters

from secml.data.loader import CDLRandomBlobs
dataset = CDLRandomBlobs(n_features=n_features, 
                         centers=centers, 
                         cluster_std=cluster_std,
                         n_samples=n_samples,
                         random_state=random_state).load()

n_tr = 100  # Number of training set samples
n_val = 100  # Number of validation set samples
n_ts = 100  # Number of test set samples

# Split in training, validation and test
from secml.data.splitter import CTrainTestSplit
splitter = CTrainTestSplit(
    train_size=n_tr + n_val, test_size=n_ts, random_state=random_state)
tr_val, ts = splitter.split(dataset)
splitter = CTrainTestSplit(
    train_size=n_tr, test_size=n_val, random_state=random_state)
tr, val = splitter.split(dataset)

# Normalize the data
from secml.ml.features import CNormalizerMinMax
nmz = CNormalizerMinMax()
tr.X = nmz.fit_transform(tr.X)
val.X = nmz.transform(val.X)
ts.X = nmz.transform(ts.X)

# Display the training set
from secml.figure import CFigure
# Only required for visualization in notebooks
%matplotlib inline

fig = CFigure(width=5, height=5)

# Convenience function for plotting a dataset
fig.sp.plot_ds(tr)

fig.show()

The task of this model is to sort the dots into their categories.

Creating and Training the Model

# Metric to use for training and performance evaluation
from secml.ml.peval.metrics import CMetricAccuracy
metric = CMetricAccuracy()

# Creation of the multiclass classifier
from secml.ml.classifiers import CClassifierSVM
from secml.ml.kernels import CKernelRBF
clf = CClassifierSVM(kernel=CKernelRBF(gamma=10), C=1)

# We can now fit the classifier
clf.fit(tr.X, tr.Y)
print("Training of classifier complete!")

# Compute predictions on a test set
y_pred = clf.predict(ts.X)

# Evaluate the accuracy of the classifier
acc = metric.performance_score(y_true=ts.Y, y_pred=y_pred)

print("Accuracy on test set: {:.2%}".format(acc))

Visualizing the Results

fig = CFigure(width=5, height=5)

# Convenience function for plotting the decision function of a classifier
fig.sp.plot_decision_regions(clf, n_grid_points=200)

fig.sp.plot_ds(ts)
fig.sp.grid(grid_on=False)

fig.sp.title("Classification regions")
fig.sp.text(0.01, 0.01, "Accuracy on test set: {:.2%}".format(acc), 
            bbox=dict(facecolor='white'))
fig.show()

Manipulating One Label

Execute these commands to change the first label:

print("Training set:", tr.Y)
tr_poisoned = tr.deepcopy()
tr_poisoned.Y[0] = 0
print("Poisoned:    ", tr_poisoned.Y)
print('X[0]: ({:.2f}, {:.2f})'.format(tr_poisoned.X.tolist()[0][0], 
                              tr_poisoned.X.tolist()[0][1]))
print()
fig = CFigure(width=9, height=4)
fig.subplot(1, 2, 1)
fig.sp.plot_ds(tr)

fig.subplot(1, 2, 2)
fig.sp.plot_ds(tr_poisoned)
fig.show()

As shown below, one red dot in the top center is now blue.

Performance of Poisoned Model

# Create the poisoned multiclass classifier
clf_poisoned = CClassifierSVM(kernel=CKernelRBF(gamma=10), C=1)

# Fit the poisoned classifier
clf_poisoned.fit(tr_poisoned.X, tr_poisoned.Y)
print("Training of poisoned classifier complete!")

# Compute predictions on a test set
y_pred_poisoned = clf_poisoned.predict(ts.X)

# Evaluate the accuracy of the classifier
acc_poisoned = metric.performance_score(y_true=ts.Y, y_pred=y_pred_poisoned)

print("Accuracy on test set before poisoning: {:.2%}".format(acc))
print("Accuracy on test set after poisoning:  {:.2%}".format(acc_poisoned))

Manipulating More Labels

print("Training set:", tr.Y)
tr_poisoned = tr.deepcopy()

for i in range(40):
  if tr_poisoned.Y[i] == 1:
    tr_poisoned.Y[i] = 0

print("Poisoned:    ", tr_poisoned.Y)
print('X[0]: ({:.2f}, {:.2f})'.format(tr_poisoned.X.tolist()[0][0], 
                              tr_poisoned.X.tolist()[0][1]))
print()
fig = CFigure(width=9, height=4)
fig.subplot(1, 2, 1)
fig.sp.plot_ds(tr)

fig.subplot(1, 2, 2)
fig.sp.plot_ds(tr_poisoned)
fig.show()

As shown below, many red dots have turned blue.

Performance of Poisoned Model

# Create the poisoned multiclass classifier
clf_poisoned = CClassifierSVM(kernel=CKernelRBF(gamma=10), C=1)

# Fit the poisoned classifier
clf_poisoned.fit(tr_poisoned.X, tr_poisoned.Y)
print("Training of poisoned classifier complete!")

# Compute predictions on a test set
y_pred_poisoned = clf_poisoned.predict(ts.X)

# Evaluate the accuracy of the classifier
acc_poisoned = metric.performance_score(y_true=ts.Y, y_pred=y_pred_poisoned)

print("Accuracy on test set before poisoning: {:.2%}".format(acc))
print("Accuracy on test set after poisoning:  {:.2%}".format(acc_poisoned))

Flag ML 109.1: Poisoning Even More Red Dots (10 pts) Adjust the attack above to test the first 60 dots in the training set, and turn the red dots blue. The flag is covered by a green rectangle in the image below.

Flag ML 109.2: Reversing Dots (10 pts) Adjust the attack above to reverse the colors of the first 40 dots in the training set, as shown below. The flag is covered by a green rectangle in the image below.

Flag ML 109.3: Poisoning Rightmost Red Dots (10 pts) Adjust the attack above to change the red dots with a horizontal coordinate greater than 0.8 blue, as shown below. The flag is covered by a green rectangle in the image below.

Sources

Posted 5-4-23
Minor update 10-7-23
"three groups" changed to "two groups" 7-24-24