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Multimodal transformer-based watermarking for deepfake detection and digital media authentication: current progress,
Kok Swee Sim1, Md Tahidul Islam1
1Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.
Frontiers in Artificial Intelligence
|July 29, 2026
Summary
Transformer models offer advanced solutions for digital watermarking and deepfake detection, addressing the limitations of traditional methods in multimodal content authentication. These models enhance media provenance and forgery detection capabilities.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Digital Forensics
Background:
- Deepfake generation technologies have rapidly advanced, outpacing current digital media forensic tools.
- Traditional watermarking methods are insufficient for complex, multimodal content ecosystems.
- Ensuring reliable media provenance is a critical challenge in applied artificial intelligence.
Purpose of the Study:
- To survey transformer-based approaches for digital watermarking and deepfake detection.
- To evaluate the effectiveness of unified architectures across multiple modalities.
- To identify advancements in integrating watermark embedding, forgery detection, and content authentication.
Main Methods:
- Review of transformer-based deep learning frameworks for media authentication.
- Analysis of attention-driven models and their resilience compared to legacy methods.
- Examination of integrated pipelines for watermark embedding and deepfake detection.
Main Results:
- Transformer models demonstrate significant resilience gains over traditional watermarking techniques.
- Unified architectures show promise for cross-modal deepfake detection and authentication.
- Integrated pipelines offer technical advantages for media provenance solutions.
Conclusions:
- Transformer models are crucial for advancing digital watermarking and deepfake detection.
- Unified, multimodal approaches are necessary for robust media authentication.
- Key challenges include cross-modal generalization, adversarial robustness, and benchmark development.