Related Experiment Video
Updated: Jul 15, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Momentum contrast learning-based multimodal digital content infringement detection.
Haibo Wu1, Jie Zhao2, Feng Zhou3
1Hangzhou Shiqu Information and Technology Co., Ltd., Hangzhou, China. sci@weshop.com.
Scientific Reports
|July 13, 2026
Summary
This study introduces MoCo-OSGF, a novel framework enhancing digital content protection. It boosts adversarial resilience and retrieval accuracy for multimodal infringement detection, reducing computational costs.
Area of Science:
- Computer Science
- Artificial Intelligence
- Digital Forensics
Background:
- Digital content infringement is rising, with common manipulations like cropping and filtering undermining detection systems.
- Traditional methods struggle with adversarial robustness due to limited data and inefficient high-dimensional descriptors.
Purpose of the Study:
- To develop a resilient and efficient framework for high-fidelity multimodal digital content infringement analysis.
- To enhance adversarial resilience and reduce retrieval complexity in content protection systems.
Main Methods:
- Utilized contrastive self-supervised representation learning with a large unlabeled multimodal dataset.
- Proposed the MoCo-OSGF framework integrating momentum contrast, orthogonal subspace learning, and global feature learning.
- Implemented advanced feature compression techniques reducing dimensionality from 2048D to 256D while preserving discriminative ability.
Main Results:
- Achieved 18-22% resilience against adversarial modifications.
- Improved large-scale retrieval accuracy by 35%.
- Reduced retrieval delay by 27.6% and computational overhead by 48.2%.
Conclusions:
- MoCo-OSGF demonstrates significant improvements in adversarial resilience and retrieval efficiency for multimodal infringement detection.
- The framework is scalable and robust, offering a promising solution for next-generation digital content protection.
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