  In recent years, neural backdoor attack has been considered to be a potential
security threat to deep learning systems. Such systems, while achieving the
state-of-the-art performance on clean data, perform abnormally on inputs with
predefined triggers. Current backdoor techniques, however, rely on uniform
trigger patterns, which are easily detected and mitigated by current defense
methods. In this work, we propose a novel backdoor attack technique in which
the triggers vary from input to input. To achieve this goal, we implement an
input-aware trigger generator driven by diversity loss. A novel cross-trigger
test is applied to enforce trigger nonreusablity, making backdoor verification
impossible. Experiments show that our method is efficient in various attack
scenarios as well as multiple datasets. We further demonstrate that our
backdoor can bypass the state of the art defense methods. An analysis with a
famous neural network inspector again proves the stealthiness of the proposed
attack. Our code is publicly available at
https://github.com/VinAIResearch/input-aware-backdoor-attack-release.
