  Backdoor attack intends to embed hidden backdoor into deep neural networks
(DNNs), such that the attacked model performs well on benign samples, whereas
its prediction will be maliciously changed if the hidden backdoor is activated
by the attacker defined trigger. This threat could happen when the training
process is not fully controlled, such as training on third-party data-sets or
adopting third-party models. There has been a lot of research and different
methods to defend such type of backdoor attacks, one being robustness-aware
perturbation-based defense method. This method mainly exploits big gap of
robustness between poisoned and clean samples. In our work, we break this
defense by controlling the robustness gap between poisoned and clean samples
using adversarial training step.
