  Class incremental learning approaches are useful as they help the model to
learn new information (classes) sequentially, while also retaining the
previously acquired information (classes). However, it has been shown that such
approaches are extremely vulnerable to the adversarial backdoor attacks, where
an intelligent adversary can introduce small amount of misinformation to the
model in the form of imperceptible backdoor pattern during training to cause
deliberate forgetting of a specific task or class at test time. In this work,
we propose a novel defensive framework to counter such an insidious attack
where, we use the attacker's primary strength-hiding the backdoor pattern by
making it imperceptible to humans-against it, and propose to learn a
perceptible (stronger) pattern (also during the training) that can overpower
the attacker's imperceptible (weaker) pattern. We demonstrate the effectiveness
of the proposed defensive mechanism through various commonly used Replay-based
(both generative and exact replay-based) class incremental learning algorithms
using continual learning benchmark variants of CIFAR-10, CIFAR-100, and MNIST
datasets. Most noteworthy, our proposed defensive framework does not assume
that the attacker's target task and target class is known to the defender. The
defender is also unaware of the shape, size, and location of the attacker's
pattern. We show that our proposed defensive framework considerably improves
the performance of class incremental learning algorithms with no knowledge of
the attacker's target task, attacker's target class, and attacker's
imperceptible pattern. We term our defensive framework as Adversary Aware
Continual Learning (AACL).
