  Deep neural networks are vulnerable to backdoor attacks. Among the existing
backdoor defense methods, trigger reverse engineering based approaches, which
reconstruct the backdoor triggers via optimizations, are the most versatile and
effective ones compared to other types of methods. In this paper, we summarize
and construct a generic paradigm for the typical trigger reverse engineering
process. Based on this paradigm, we propose a new perspective to defeat trigger
reverse engineering by manipulating the classification confidence of backdoor
samples. To determine the specific modifications of classification confidence,
we propose a compensatory model to compute the lower bound of the modification.
With proper modifications, the backdoor attack can easily bypass the trigger
reverse engineering based methods. To achieve this objective, we propose a
Label Smoothing Poisoning (LSP) framework, which leverages label smoothing to
specifically manipulate the classification confidences of backdoor samples.
Extensive experiments demonstrate that the proposed work can defeat the
state-of-the-art trigger reverse engineering based methods, and possess good
compatibility with a variety of existing backdoor attacks.
