%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Kumar_INTERSPEECH_2026,
                      author = {Kumar, Vishal and Abrol, Vinayak and Magimai-Doss, Mathew},
                    keywords = {Anti-spoofing, Open-World Recognition, Synthetic Speech Attribution},
                    projects = {Idiap, PaSS},
         mainresearchprogram = {Sustainable & Resilient Societies},
                       title = {Who Synthesized This? Joint Deepfake Detection and Generative Source Attribution},
                   booktitle = {Proceedings of Interspeech},
                        year = {2026},
                         doi = {10.21437/Interspeech.2026-2442},
                    abstract = {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.}
}