  We have witnessed the continuing arms race between backdoor attacks and the
corresponding defense strategies on Deep Neural Networks (DNNs). Most
state-of-the-art defenses rely on the statistical sanitization of the "inputs"
or "latent DNN representations" to capture trojan behaviour. In this paper, we
first challenge the robustness of such recently reported defenses by
introducing a novel variant of targeted backdoor attack, called "low-confidence
backdoor attack". We also propose a novel defense technique, called "HaS-Nets".
  "Low-confidence backdoor attack" exploits the confidence labels assigned to
poisoned training samples by giving low values to hide their presence from the
defender, both during training and inference. We evaluate the attack against
four state-of-the-art defense methods, viz., STRIP, Gradient-Shaping, Februus
and ULP-defense, and achieve Attack Success Rate (ASR) of 99%, 63.73%, 91.2%
and 80%, respectively.
  We next present "HaS-Nets" to resist backdoor insertion in the network during
training, using a reasonably small healing dataset, approximately 2% to 15% of
full training data, to heal the network at each iteration. We evaluate it for
different datasets - Fashion-MNIST, CIFAR-10, Consumer Complaint and Urban
Sound - and network architectures - MLPs, 2D-CNNs, 1D-CNNs. Our experiments
show that "HaS-Nets" can decrease ASRs from over 90% to less than 15%,
independent of the dataset, attack configuration and network architecture.
