  Recently, a special type of data poisoning (DP) attack targeting Deep Neural
Network (DNN) classifiers, known as a backdoor, was proposed. These attacks do
not seek to degrade classification accuracy, but rather to have the classifier
learn to classify to a target class whenever the backdoor pattern is present in
a test example. Launching backdoor attacks does not require knowledge of the
classifier or its training process - it only needs the ability to poison the
training set with (a sufficient number of) exemplars containing a sufficiently
strong backdoor pattern (labeled with the target class). Here we address
post-training detection of backdoor attacks in DNN image classifiers, seldom
considered in existing works, wherein the defender does not have access to the
poisoned training set, but only to the trained classifier itself, as well as to
clean examples from the classification domain. This is an important scenario
because a trained classifier may be the basis of e.g. a phone app that will be
shared with many users. Detecting backdoors post-training may thus reveal a
widespread attack. We propose a purely unsupervised anomaly detection (AD)
defense against imperceptible backdoor attacks that: i) detects whether the
trained DNN has been backdoor-attacked; ii) infers the source and target
classes involved in a detected attack; iii) we even demonstrate it is possible
to accurately estimate the backdoor pattern. We test our AD approach, in
comparison with alternative defenses, for several backdoor patterns, data sets,
and attack settings and demonstrate its favorability. Our defense essentially
requires setting a single hyperparameter (the detection threshold), which can
e.g. be chosen to fix the system's false positive rate.
