DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement...

Area of Science:

• Artificial Intelligence
• Computer Vision
• Digital Forensics

Background:

• Generative AI advancements create realistic deepfakes, challenging detection and public trust.
• Passive deepfake detectors lack generalization due to reliance on specific forgery artifacts.
• Proactive watermarking offers a solution but struggles to balance robustness and tamper sensitivity.

Purpose of the Study:

• To develop a novel deep learning framework for robust and adaptive deepfake watermarking.
• To create a watermarking approach that balances robustness against benign distortions and sensitivity to malicious tampering.
• To improve the detection of high-quality synthetic media.

Main Methods:

• A deep learning framework utilizing high-dimensional latent space representations.
• An Adversarial Reinforcement Learning (ARL) paradigm for adaptive watermarking.
• A learnable watermark embedder operating in the latent space for semantic capture and controlled encoding/extraction.
• Simulation of benign and malicious image manipulations via an adversarial attacker agent.

Main Results:

• The proposed method consistently outperforms state-of-the-art approaches on CelebA and CelebA-HQ benchmarks.
• Achieved over 4.5% improvement on CelebA and over 5.3% on CelebA-HQ under challenging manipulation scenarios.
• Demonstrated effective balance between watermark robustness and fragility through ARL.

Conclusions:

• The novel ARL-based watermarking framework offers a robust and adaptive solution for deepfake detection.
• The method shows significant improvements in identifying synthetic media, even under adversarial conditions.
• This approach enhances the reliability of digital media authentication in the face of advanced AI generation techniques.