AV-DeepFake-Net: Attention-Guided and Uncertainty-Aware Network for Audiovisual DeepFake Detection

Abstract

Detecting audio-visual DeepFake (AVDeepFake) is becoming increasingly important as synthetic media tools become widely accessible and spread across consumer devices. In this study, we present an Detecting audio-visual DeepFakes (AV-DeepFakes) has become increasingly critical with the rapid proliferation of accessible synthetic media generation tools across consumer platforms. In this work, we propose a highperformance, deployment-efficient AV-DeepFake detection framework tailored for real-world consumer devices. The proposed model integrates a 3D convolutional visual encoder with a 2D convolutional audio encoder to learn synchronized multimodal representations, effectively capturing spatial, spectral, and prosodic inconsistencies inherent in manipulated content. To detect temporal forgeries, we introduce a bidirectional complementary boundary module that precisely localizes manipulation onsets and offsets. A cross-modal attention fusion mechanism aggregates modality-specific cues, while an uncertainty-aware gating strategy suppresses unreliable signals to improve robustness. Furthermore, a cross-modal discrepancy minimization loss encourages alignment for genuine samples while maximizing divergence for forged content, strengthening multimodal consistency learning. Extensive evaluations on FaceForensics++ and LAV-DF demonstrate the effectiveness of the proposed approach, achieving 97.1% AUC for clip-level detection and an 81.2% F1-score for temporal boundary localization, while reducing inference time by 5× compared with transformer-based methods.