Robust deepfake detector against deep image watermarking
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new deepfake detection model that performs well even with watermarked images. The model shows improved accuracy against FaceSigns watermarks, outperforming existing methods.
Area Of Science
- Computer Science
- Information Security
- Artificial Intelligence
Background
- Deepfake technology presents a growing threat to information security.
- Existing deepfake detection methods often fail when images contain deep watermarks.
- Watermarking techniques like MBRS and FaceSigns can degrade detection performance.
Purpose Of The Study
- To develop a robust deepfake detection model resistant to image watermarking.
- To improve the accuracy of deepfake detection in the presence of common watermarking algorithms.
Main Methods
- Proposed a multi-module deepfake detection model.
- Integrated Efficient Multi-scale Attention within the Xception architecture.
- Introduced a feature dropout module to remove redundant image features.
Main Results
- The model achieved comparable accuracy to baseline models with MBRS watermarks.
- The model significantly outperformed baseline models with FaceSigns watermarks, showing 10% and 20% higher accuracy at 50% and 100% watermark presence, respectively.
- The feature dropout module effectively eliminated redundant image features.
Conclusions
- The proposed model demonstrates enhanced robustness against deep watermarking in deepfake images.
- The integration of Efficient Multi-scale Attention and feature dropout improves detection performance, particularly against FaceSigns watermarks.
- This research contributes to more reliable deepfake detection systems in real-world scenarios.
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