Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection

TL;DR

This paper introduces DFLIP-3K, a comprehensive open database with 300K deepfake samples and 190K linguistic footprints, enabling advanced, explainable deepfake detection and model identification through linguistic profiling.

Contribution

The paper presents the largest deepfake database with linguistic annotations, facilitating next-generation, interpretable deepfake detection methods and establishing a benchmark for linguistic profiling.

Findings

DFLIP-3K contains 300K deepfake samples from 3K models.

The database includes 190K linguistic footprints for deepfake analysis.

Experiments show DFLIP-3K effectively supports linguistic-based detection and identification.

Abstract

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.