We are developing an advanced deepfake defense tool as a BAM (Brand Abuse Monitoring) solution to protect corporate media assets from manipulation and misuse. The system integrates self-verification watermarking using an invertible neural network (INN) to embed a unique hidden digital signature (fingerprint) into images and videos. A machine learning-based deepfake detector continuously scans assets, comparing the embedded fingerprint with a freshly computed latent code to identify unauthorized modifications. A real-time anomaly detection engine triggers alerts if discrepancies are found, ensuring asset integrity. Additionally, the tool integrates with threat intelligence feeds to update its detection capabilities against emerging deepfake threats. A dashboard and incident response module provides security teams with real-time alerts, historical logs, and automated workflows for rapid mitigation. By combining ML-driven deepfake detection, watermark-based integrity verification, and threat intelligence, our tool offers a comprehensive, enterprise-grade cybersecurity solution to combat deepfake-based fraud and digital asset tampering.