Detecting Localized Deepfake Manipulations Using Action Unit-Guided Video Representations

• URL: http://arxiv.org/abs/2503.22121v2
• Date: Sun, 13 Apr 2025 18:17:29 GMT
• Title: Detecting Localized Deepfake Manipulations Using Action Unit-Guided Video Representations
• Authors: Tharun Anand, Siva Sankar Sajeev, Pravin Nair,
• Abstract summary: Deepfake techniques are increasingly narrowing the gap between real and synthetic videos, raising serious privacy and security concerns.<n>This work presents the first detection approach explicitly designed to generalize to localized edits in deepfake videos.<n>Our method achieves a $20%$ improvement in accuracy over current state-of-the-art detection methods.
• Score: 4.449835214520726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With rapid advancements in generative modeling, deepfake techniques are increasingly narrowing the gap between real and synthetic videos, raising serious privacy and security concerns. Beyond traditional face swapping and reenactment, an emerging trend in recent state-of-the-art deepfake generation methods involves localized edits such as subtle manipulations of specific facial features like raising eyebrows, altering eye shapes, or modifying mouth expressions. These fine-grained manipulations pose a significant challenge for existing detection models, which struggle to capture such localized variations. To the best of our knowledge, this work presents the first detection approach explicitly designed to generalize to localized edits in deepfake videos by leveraging spatiotemporal representations guided by facial action units. Our method leverages a cross-attention-based fusion of representations learned from pretext tasks like random masking and action unit detection, to create an embedding that effectively encodes subtle, localized changes. Comprehensive evaluations across multiple deepfake generation methods demonstrate that our approach, despite being trained solely on the traditional FF+ dataset, sets a new benchmark in detecting recent deepfake-generated videos with fine-grained local edits, achieving a $20\%$ improvement in accuracy over current state-of-the-art detection methods. Additionally, our method delivers competitive performance on standard datasets, highlighting its robustness and generalization across diverse types of local and global forgeries.

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