CONF Kumar_INTERSPEECH_2026/IDIAP Who Synthesized This? Joint Deepfake Detection and Generative Source Attribution Kumar, Vishal Abrol, Vinayak Magimai-Doss, Mathew Anti-spoofing Open-World Recognition Synthetic Speech Attribution Proceedings of Interspeech 2026 10.21437/Interspeech.2026-2442 doi We propose a few-shot, open-set framework for joint deepfake detection and generative source attribution. Addressing the rapid evolution of synthetic speech, we introduce a temporal evaluation strategy using the MLAAD v9 dataset, partitioning models by launch year and architectural family. Our model utilizes a WavLM-Large model with targeted LoRA adapters, optimized via-a hierarchical metric learning objective: Conditional AAM Softmax + Center Loss. We simulate real-world forensic deployment by performing prototype-based inference on unseen 2025+ model families, establishing new class centroids with only 12 samples. Evaluated on the ASVspoof 5.0 benchmark, our framework achieves SOTA performance with 0.49% EER and 0.09 minDCF, while attaining 99% accuracy in tracing generated speech to its source. These results demonstrate that hierarchical representation learning and few-shot prototype adaptation provide a robust, scalable defense against the shifting landscape of audio synthesis.