  Deception detection has attracted increasing attention due to its importance
in real-world scenarios. Its main goal is to detect deceptive behaviors from
multimodal clues such as gestures, facial expressions, prosody, etc. However,
these bases are usually subjective and related to personal habits. Therefore,
we extend deception detection to deception reasoning, further providing
objective evidence to support subjective judgment. Specifically, we provide
potential lies and basic facts and then analyze why this sentence may be a lie
by combining factual inconsistencies and intent behind them. Compared with
deception detection, this task is more applicable to real-world scenarios. For
example, in interrogation, the police should judge whether a person is lying
based on solid evidence. This paper presents our initial attempts at this task,
including constructing a dataset and defining evaluation metrics. Meanwhile,
this task can serve as a benchmark for evaluating the complex reasoning
capability of large language models. Code and data will be made publicly
available.
