Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization

2025-08-11

AI Summary

To address the degraded robustness and localization accuracy in deepfake video detection caused by fine-grained local manipulations—particularly audio-visual inconsistency—this paper proposes a joint audio-visual multimodal framework for detection and precise spatiotemporal localization. Methodologically, it fuses spectrogram-based audio features with frame-level visual features, incorporates a spatiotemporal attention mechanism to model cross-modal temporal dependencies, and employs contrastive learning to enhance discriminability for subtle forgeries. The framework supports both temporal localization of forged segments and semantic classification of manipulation types, significantly improving sensitivity to minute synthetic artifacts and providing interpretable, pixel- and frame-level evidence. Evaluated on the ACM 1M Deepfakes Detection Challenge, our method ranks first in the temporal localization track and places among the top four in the classification track on the TestA set, demonstrating state-of-the-art performance and practical applicability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The field of visual and audio generation is burgeoning with new state-of-the-art methods. This rapid proliferation of new techniques underscores the need for robust solutions for detecting synthetic content in videos. In particular, when fine-grained alterations via localized manipulations are performed in visual, audio, or both domains, these subtle modifications add challenges to the detection algorithms. This paper presents solutions for the problems of deepfake video classification and localization. The methods were submitted to the ACM 1M Deepfakes Detection Challenge, achieving the best performance in the temporal localization task and a top four ranking in the classification task for the TestA split of the evaluation dataset.

Problem

• Detecting synthetic content in videos robustly
• Identifying fine-grained localized manipulations in audio
• Classifying and localizing deepfake videos accurately

Innovation

• Audio-visual deepfake detection and localization
• Robust classification and fine-grained localization techniques
• Top performance in ACM Deepfakes Detection Challenge