Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario

计算机科学 离散余弦变换 人工智能 稳健性(进化) 判别式 特征提取 频域 模式识别(心理学) 计算机视觉 特征(语言学) 图像(数学) 语言学 生物化学 基因 哲学 化学
作者
Yukai WANG,Chunlei Peng,Decheng Liu,Nannan Wang,Xinbo Gao
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology [Institute of Electrical and Electronics Engineers]
卷期号:33 (12): 7943-7956 被引量:8
标识
DOI:10.1109/tcsvt.2023.3281475
摘要

In recent years, with the rapid development of face editing and generation, more and more fake videos are circulating on social media, which has caused extreme public concerns. Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum. But for synthesized videos, these methods only confine to a single frame and pay little attention to the most discriminative part and temporal frequency clue among different frames. To take full advantage of the rich information in video sequences, this paper performs video forgery detection on both spatial and temporal frequency domains and proposes a Discrete Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spectrum spatial-temporal feature representation. FCAN-DCT totally consists of a backbone network and two branches: Compact Feature Extraction (CFE) module and Frequency Temporal Attention (FTA) module. We conduct thorough experimental assessments on three visible light (VIS) based datasets (i.e.,, FaceForensics++, Celeb-DF (v2), WildDeepfake), and our self-built video forgery dataset DeepfakeNIR, which is the first video forgery dataset on near-infrared (NIR) modality. The experimental results demonstrate the effectiveness and robustness of our method for detecting forgery videos in both VIS and NIR scenarios.DeepfakeNIR and code are available at https://github.com/AEP-WYK/DeepfakeNIR .
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