DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Multi-Agent Adversarial Reinforcement Learning
Published 7 Nov 2025 · arXiv · Tharindu Fernando
Overview
DeepForgeSeal is a new framework for detecting deepfakes using latent space-driven semi-fragile watermarking. It leverages Multi-Agent Adversarial Reinforcement Learning (MAARL) to improve the robustness and sensitivity of watermarking techniques.
Key Insights
- Watermarking Framework: Utilizes high-dimensional latent space representations for watermark embedding, enhancing detection capabilities.
- Performance: Outperforms state-of-the-art methods by over 4.5% on CelebA and 5.3% on CelebA-HQ benchmarks.
- Methodology: Employs MAARL to dynamically balance robustness against benign distortions and sensitivity to malicious tampering.
BFSI Relevance
- Why Relevant: Enhances the ability to detect and manage deepfake threats, crucial for maintaining trust in digital media.
- Primary Sector: Financial Services
- Subsectors: Fraud Detection, Cybersecurity
- Actionable Implications: BFSI professionals should explore integrating advanced watermarking techniques to bolster fraud detection systems.
researcher peer-reviewed-paper global