UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization (2308.14395v1)

Abstract: The emergence of artificial intelligence-generated content (AIGC) has raised concerns about the authenticity of multimedia content in various fields. However, existing research for forgery content detection has focused mainly on binary classification tasks of complete videos, which has limited applicability in industrial settings. To address this gap, we propose UMMAFormer, a novel universal transformer framework for temporal forgery localization (TFL) that predicts forgery segments with multimodal adaptation. Our approach introduces a Temporal Feature Abnormal Attention (TFAA) module based on temporal feature reconstruction to enhance the detection of temporal differences. We also design a Parallel Cross-Attention Feature Pyramid Network (PCA-FPN) to optimize the Feature Pyramid Network (FPN) for subtle feature enhancement. To evaluate the proposed method, we contribute a novel Temporal Video Inpainting Localization (TVIL) dataset specifically tailored for video inpainting scenes. Our experiments show that our approach achieves state-of-the-art performance on benchmark datasets, including Lav-DF, TVIL, and Psynd, significantly outperforming previous methods. The code and data are available at https://github.com/ymhzyj/UMMAFormer/.

• The paper presents UMMAFormer, a transformer-based framework that enhances temporal forgery localization via TFAA and PCA-FPN modules.
• It demonstrates robust performance across benchmarks like Lav-DF, TVIL, and Psynd, achieving state-of-the-art accuracy in detecting multimedia forgeries.
• The approach offers practical insights for multimedia security and lays the groundwork for advancing digital forensics research.

Analysis of "UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization"

The presented paper explores a significant problem associated with the proliferation of artificial intelligence-generated content (AIGC) by developing a sophisticated method for detecting multimedia manipulation through temporal forgery localization (TFL). The authors propose UMMAFormer, an innovative framework combining a universal multimodal-adaptive transformer with refined components for effective localization, targeting varied types of multimedia inputs.

Methodology and Framework

At the heart of this research is the UMMAFormer framework, which harnesses the flexibility and power of transformer-based architectures to analyze multimodal data. UMMAFormer comprises three principal modules: a pre-trained feature extractor, a feature enhancement module using the Temporal Feature Abnormal Attention (TFAA) methodology, and a Parallel Cross-Attention Feature Pyramid Network (PCA-FPN).

1. Temporal Feature Abnormal Attention (TFAA): This module utilizes a reconstruction learning strategy to heighten the model's sensitivity to temporal forgeries. It effectively discriminates between manipulated and real samples by reconstructing features and examining deviations. TFAA emphasizes identifying temporal discrepancies arising from spatial modifications, fortifying the detection capabilities in various multimedia contexts.
2. Parallel Cross-Attention Feature Pyramid Network (PCA-FPN): This advanced feature pyramid structure enhances the detection of minor or ultrashort forgeries across different resolution features. By leveraging parallel processing and cross-attention mechanisms, PCA-FPN mitigates noise introduction that typically plagues traditional FPN approaches, significantly enhancing the localization accuracy for subtle feature transients.

Empirical Evaluation

The authors validate UMMAFormer on multiple benchmark datasets, including Lav-DF, TVIL, and Psynd, where it consistently delivers superior results. Particularly notable is the model's performance in the multimodal Lav-DF full set, where it achieves state-of-the-art results, markedly enhancing AP scores at various tIoU thresholds compared to previous works like BA-TFD. Such performance reflects its robust detection and localization capability, even in complex multimedia environments comprising both visual and auditory components.

The creation of the TVIL dataset, designed to simulate real-world threats posed by video inpainting, provides a novel contribution to the field. This dataset fosters a broader understanding of general forgery threats beyond facial manipulations, expanding the scope and relevance of TFL research.

Implications and Future Directions

UMMAFormer illustrates a promising direction for multimedia security research, addressing practical challenges in authenticating content integrity across audiovisual platforms. The innovative feature enhancement and adaptation strategies exemplify the sophisticated techniques required to combat sophisticated forgery methods. The ability to accurately localize manipulative segments within content has profound implications for multimedia forensics and verifying the authenticity of digital media.

Future developments could explore spatial forgery localization to complement the current temporal focus, further enhancing the framework's practical utility. Integrating these advancements could facilitate more comprehensive solutions for digital forensics, potentially extending to other emerging areas where multimedia authenticity is of paramount concern.

By navigating complexities inherent to multimodal data and forging new benchmarks with datasets like TVIL, this research represents a substantial contribution to the ongoing discourse on combating the misuse of AI-generated content.