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Recent advancements in technologies, such as the 'deepfake' technique, havepaved the way for the generation of various media forgeries. In response to thepotential hazards of these media forgeries, many researchers engage inexploring detection methods, increasing the demand for high-quality mediaforgery datasets. Despite this, existing datasets have certain limitations.Firstly, most of datasets focus on the manipulation of visual modality andusually 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, while manyreal-world forgeries are driven by identity, the identity information of thesubject in media is frequently neglected. For detection, identity informationcould be an essential clue to boost accuracy. Moreover, official mediaconcerning certain identities on the Internet can serve as prior knowledge,aiding both the audience and forgery detectors in determining the trueidentity. Therefore, we propose an identity-driven multimedia forgery dataset,IDForge, which contains 249,138 video shots. All video shots are sourced from324 wild videos collected of 54 celebrities from the Internet. The fake videoshots involve 9 types of manipulation across visual, audio and textualmodalities. Additionally, IDForge provides extra 214,438 real video shots as areference set for the 54 celebrities. Correspondingly, we design an effectivemultimedia detection network, Reference-assisted Multimodal Forgery DetectionNetwork (R-MFDN). Through extensive experiments on the proposed dataset, wedemonstrate the effectiveness of R-MFDN on the multimedia detection task.

Identity-Driven Multimedia Forgery Detection via Reference Assistance