Emotion-Aware multimodal deepfake detection.

Area of Science:

• Computer Science
• Artificial Intelligence
• Multimedia Security

Background:

• Deepfake technology advancements challenge traditional unimodal detection methods.
• Existing multimodal deepfake detection often requires extensive training data, limiting few-shot generalization.
• Multimodal manipulations present unique challenges not addressed by single-modal analysis.

Purpose of the Study:

• To propose an emotion-aware multimodal deepfake detection method.
• To enhance deepfake detection robustness in few-shot and cross-identity scenarios.
• To leverage emotion signals and cross-modal interactions for improved forgery detection.

Main Methods:

• Developed an emotion embedding extractor (Emoencoder) for modality-specific emotion representation.
• Implemented Emotion-Aware Contrastive Learning and Cross-Modal Contrastive Learning to capture inter-modal inconsistencies.
• Utilized a Text-Guided Semantic Fusion module for audio-visual feature interaction guided by text semantics.
• Employed a cross-identity few-shot training strategy for data-limited validation.

Main Results:

• The proposed method demonstrates superior performance compared to state-of-the-art (SOTA) techniques.
• Achieved robust generalization capabilities for unseen identities.
• Showcased effective detection of unseen manipulation types.
• Validated effectiveness under data-limited conditions.

Conclusions:

• Emotion-aware multimodal analysis offers a promising direction for advanced deepfake detection.
• The developed fusion strategy effectively integrates cross-modal information.
• The method shows significant potential for real-world applications requiring robust deepfake detection with limited data.