The field of deepfake detection and privacy preservation is moving towards more robust and adaptive approaches. Researchers are exploring the use of latent space representations, multi-agent adversarial reinforcement learning, and decoupled architectures to improve the detection of AI-generated content. Additionally, there is a growing focus on privacy preservation, with methods being developed to anonymize video features and remove sensitive information. These advancements have the potential to improve the security and trustworthiness of digital media. Noteworthy papers include: DeepForgeSeal, which introduces a novel deep learning framework for robust and adaptive watermarking, and OmniAID, which proposes a decoupled Mixture-of-Experts architecture for universal AI-generated image detection. DBINDS is also notable for its use of diffusion-model-inversion to analyze latent-space dynamics for AI-generated video detection. Privacy Beyond Pixels is another significant work, introducing a lightweight Anonymizing Adapter Module for privacy-preserving video understanding.