Towards Explainable and Robust Deepfake Detection and Attribution: Enhancing Multimedia Forensics for the Next Generation of Synthetic Media

The rise of generative AI has enabled the creation of synthetic audio, images, and videos that are virtually indistinguishable from authentic media, presenting new threats to digital trust, privacy, and security. While deepfake detection has advanced, most solutions focus on binary classification performed by data-driven approaches, which are insufficient for attribution and explainability required in high-stakes scenarios. This dissertation aims to develop robust, generalizable, and explainable forensic frameworks that (1) not only detect AI-generated media but also attribute attacks to specific models or methods, (2) provide interpretable evidence for forensic and legal contexts, and (3) are resilient to adversarial manipulations. Our approach integrates data-driven and model-based techniques, leverages external data, and builds on extensive prior work in multimedia forensics. In this paper, we present key challenges, objectives and early findings that support the advancement of trustworthy AI and strengthen digital media security.