  Recent advancements in "deepfake" techniques have paved the way for
generating various media forgeries. In response to the potential hazards of
these media forgeries, many researchers engage in exploring detection methods,
increasing the demand for high-quality media forgery datasets. Despite this,
existing datasets have certain limitations. Firstly, most datasets focus on
manipulating visual modality and usually lack diversity, as only a few forgery
approaches are considered. Secondly, the quality of media is often inadequate
in clarity and naturalness. Meanwhile, the size of the dataset is also limited.
Thirdly, it is commonly observed that real-world forgeries are motivated by
identity, yet the identity information of the individuals portrayed in these
forgeries within existing datasets remains under-explored. For detection,
identity information could be an essential clue to boost performance. Moreover,
official media concerning relevant identities on the Internet can serve as
prior knowledge, aiding both the audience and forgery detectors in determining
the true identity. Therefore, we propose an identity-driven multimedia forgery
dataset, IDForge, which contains 249,138 video shots sourced from 324 wild
videos of 54 celebrities collected from the Internet. The fake video shots
involve 9 types of manipulation across visual, audio, and textual modalities.
Additionally, IDForge provides extra 214,438 real video shots as a reference
set for the 54 celebrities. Correspondingly, we propose the Reference-assisted
Multimodal Forgery Detection Network (R-MFDN), aiming at the detection of
deepfake videos. Through extensive experiments on the proposed dataset, we
demonstrate the effectiveness of R-MFDN on the multimedia detection task.
