Linguistic Profiling of Deepfakes: An Open Database for Next-Generation
  Deepfake Detection
Yabin Wang Zhiwu Huang Zhiheng Ma Xiaopeng Hong
The emergence of text-to-image generative models has revolutionized the field
of deepfakes, enabling the creation of realistic and convincing visual content
directly from textual descriptions. However, this advancement presents
considerably greater challenges in detecting the authenticity of such content.
Existing deepfake detection datasets and methods often fall short in
effectively capturing the extensive range of emerging deepfakes and offering
satisfactory explanatory information for detection. To address the significant
issue, this paper introduces a deepfake database (DFLIP-3K) for the development
of convincing and explainable deepfake detection. It encompasses about 300K
diverse deepfake samples from approximately 3K generative models, which boasts
the largest number of deepfake models in the literature. Moreover, it collects
around 190K linguistic footprints of these deepfakes. The two distinguished
features enable DFLIP-3K to develop a benchmark that promotes progress in
linguistic profiling of deepfakes, which includes three sub-tasks namely
deepfake detection, model identification, and prompt prediction. The deepfake
model and prompt are two essential components of each deepfake, and thus
dissecting them linguistically allows for an invaluable exploration of
trustworthy and interpretable evidence in deepfake detection, which we believe
is the key for the next-generation deepfake detection. Furthermore, DFLIP-3K is
envisioned as an open database that fosters transparency and encourages
collaborative efforts to further enhance its growth. Our extensive experiments
on the developed benchmark verify that our DFLIP-3K database is capable of
serving as a standardized resource for evaluating and comparing
linguistic-based deepfake detection, identification, and prompt prediction
techniques.