arXiv subjects

Janne Laakkonen

Publications and source records attributed to Janne Laakkonen.

4 recordsLinked to original sources

arXiv2026

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection

Meta-learning for domain generalization (MLDG) improves out-of-distribution speech deepfake detection over empirical risk minimization (ERM) when both objectives train low-rank adapters on the same frozen self-supervised speech model. Because the architecture and adapter capacity are held fixed, this gap points to differences in how the training objective shapes the adapter, yet the field characterizes objectives through error rates rather than through the geometry of the solution they reach. We introduce a descriptive diagnostic for this question: holding architecture, rank, data, and seeds fixed and varying only the objective, we use the empirical Fisher on the finished adapter to compare the geometry that ERM and MLDG leave behind. We characterize each adapter with effective-rank diagnostics that separate where the adapter changes from where those changes matter to the loss, resolved by projection and by depth. Applied to ERM and MLDG, the diagnostic shows that the objective does not reshape all adapter projections alike: the loss-relevant update concentrates in the query and key projections while becoming more distributed in the output projection, consistently across six corpora and most strongly in the upper layers. The same contrast appears in the merged update independently of the low-rank factorization, indicating that it reflects the geometry of the effective update rather than the parameterization. These results show that the gap between ERM and MLDG is not only a difference in error rate, but a difference in how loss-relevant capacity is organized inside the adapter, and that loss-aware adapter geometry is a way to see it.

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arXiv2025

Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection

Foundation models such as Wav2Vec2 excel at representation learning in speech tasks, including audio deepfake detection. However, after being fine-tuned on a fixed set of bonafide and spoofed audio clips, they often fail to generalize to novel deepfake methods not represented in training. To address this, we propose a mixture-of-LoRA-experts approach that integrates multiple low-rank adapters (LoRA) into the model's attention layers. A routing mechanism selectively activates specialized experts, enhancing adaptability to evolving deepfake attacks. Experimental results show that our method outperforms standard fine-tuning in both in-domain and out-of-domain scenarios, reducing equal error rates relative to baseline models. Notably, our best MoE-LoRA model lowers the average out-of-domain EER from 8.55\% to 6.08\%, demonstrating its effectiveness in achieving generalizable audio deepfake detection.

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arXiv2025

Generalizable speech deepfake detection via meta-learned LoRA

Reliable detection of speech deepfakes (spoofs) must remain effective when the distribution of spoofing attacks shifts. We frame the task as domain generalization and show that inserting Low-Rank Adaptation (LoRA) adapters into every attention head of a self-supervised (SSL) backbone, then training only those adapters with Meta-Learning Domain Generalization (MLDG), yields strong zero-shot performance. The resulting model updates about 3.6 million parameters, roughly 1.1% of the 318 million updated in full fine-tuning, yet surpasses a fully fine-tuned counterpart on five of six evaluation corpora. A first-order MLDG loop encourages the adapters to focus on cues that persist across attack types, lowering the average EER from 8.84% for the fully fine-tuned model to 5.30% with our best MLDG-LoRA configuration. Our findings show that combining meta-learning with parameter-efficient adaptation offers an effective method for zero-shot, distribution-shift-aware speech deepfake detection.

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arXiv2024

Meta-Learning Approaches for Improving Detection of Unseen Speech Deepfakes

Current speech deepfake detection approaches perform satisfactorily against known adversaries; however, generalization to unseen attacks remains an open challenge. The proliferation of speech deepfakes on social media underscores the need for systems that can generalize to unseen attacks not observed during training. We address this problem from the perspective of meta-learning, aiming to learn attack-invariant features to adapt to unseen attacks with very few samples available. This approach is promising since generating of a high-scale training dataset is often expensive or infeasible. Our experiments demonstrated an improvement in the Equal Error Rate (EER) from 21.67% to 10.42% on the InTheWild dataset, using just 96 samples from the unseen dataset. Continuous few-shot adaptation ensures that the system remains up-to-date.

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