TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection

TL;DR

This paper introduces TT-DF, a large-scale diffusion-based dataset for human body forgery detection, along with a novel detection model, addressing the lack of datasets and methods in this domain.

Contribution

The paper presents the first large-scale human body forgery dataset (TT-DF) and a new detection model (TOF-Net) that exploits spatiotemporal inconsistencies for improved detection.

Findings

TOF-Net outperforms existing facial forgery detection models on TT-DF.

TT-DF includes diverse forgery methods and configurations for comprehensive training.

The dataset and model enhance the detection of human body forgeries in real-world scenarios.

Abstract

The emergence and popularity of facial deepfake methods spur the vigorous development of deepfake datasets and facial forgery detection, which to some extent alleviates the security concerns about facial-related artificial intelligence technologies. However, when it comes to human body forgery, there has been a persistent lack of datasets and detection methods, due to the later inception and complexity of human body generation methods. To mitigate this issue, we introduce TikTok-DeepFake (TT-DF), a novel large-scale diffusion-based dataset containing 6,120 forged videos with 1,378,857 synthetic frames, specifically tailored for body forgery detection. TT-DF offers a wide variety of forgery methods, involving multiple advanced human image animation models utilized for manipulation, two generative configurations based on the disentanglement of identity and pose information, as well as different compressed versions. The aim is to simulate any potential unseen forged data in the wild as comprehensively as possible, and we also furnish a benchmark on TT-DF. Additionally, we propose an adapted body forgery detection model, Temporal Optical Flow Network (TOF-Net), which exploits the spatiotemporal inconsistencies and optical flow distribution differences between natural data and forged data. Our experiments demonstrate that TOF-Net achieves favorable performance on TT-DF, outperforming current state-of-the-art extendable facial forgery detection models. For our TT-DF dataset, please refer to https://github.com/HashTAG00002/TT-DF.