Identity-Driven Multimedia Forgery Detection via Reference Assistance
CoRR(2024)
摘要
Recent advancements in technologies, such as the 'deepfake' technique, have
paved the way for the generation of 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 of datasets focus on the manipulation of 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, while many
real-world forgeries are driven by identity, the identity information of the
subject in media is frequently neglected. For detection, identity information
could be an essential clue to boost accuracy. Moreover, official media
concerning certain 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. All video shots are sourced from
324 wild videos collected of 54 celebrities 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 design an effective
multimedia detection network, Reference-assisted Multimodal Forgery Detection
Network (R-MFDN). Through extensive experiments on the proposed dataset, we
demonstrate the effectiveness of R-MFDN on the multimedia detection task.
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