  Automated deception detection is crucial for assisting humans in accurately
assessing truthfulness and identifying deceptive behavior. Conventional
contact-based techniques, like polygraph devices, rely on physiological signals
to determine the authenticity of an individual's statements. Nevertheless,
recent developments in automated deception detection have demonstrated that
multimodal features derived from both audio and video modalities may outperform
human observers on publicly available datasets. Despite these positive
findings, the generalizability of existing audio-visual deception detection
approaches across different scenarios remains largely unexplored. To close this
gap, we present the first cross-domain audio-visual deception detection
benchmark, that enables us to assess how well these methods generalize for use
in real-world scenarios. We used widely adopted audio and visual features and
different architectures for benchmarking, comparing single-to-single and
multi-to-single domain generalization performance. To further exploit the
impacts using data from multiple source domains for training, we investigate
three types of domain sampling strategies, including domain-simultaneous,
domain-alternating, and domain-by-domain for multi-to-single domain
generalization evaluation. Furthermore, we proposed the Attention-Mixer fusion
method to improve performance, and we believe that this new cross-domain
benchmark will facilitate future research in audio-visual deception detection.
Protocols and source code are available at
\href{https://github.com/Redaimao/cross_domain_DD}{https://github.com/Redaimao/cross\_domain\_DD}.
