# From Sharpness to Better Generalization for Speech Deepfake Detection

arXiv: https://arxiv.org/abs/2506.11532 · HTML: https://mlsanity.com/paper/2506.11532/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2025.

## Built on by (most cited first, 5 of 5)

- [Addressing Gradient Misalignment in Data-Augmented Training for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.20682/) (2025)
- [Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks](https://mlsanity.com/paper/2604.10202/) (2026)
- [Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks](https://mlsanity.com/paper/2606.28662/) (2026)
- [From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection](https://mlsanity.com/paper/2609.08899/) (2026)
- [Domain-Adaptive Dual-Gating Mixture of Experts for Generalizable Speech Deepfake Detection](https://mlsanity.com/paper/2609.33709/) (2026)

## Abstract (arXiv)

Generalization remains a critical challenge in speech deepfake detection (SDD). While various approaches aim to improve robustness, generalization is typically assessed through performance metrics like equal error rate without a theoretical framework to explain model performance. This work investigates sharpness as a theoretical proxy for generalization in SDD. We analyze how sharpness responds to domain shifts and find it increases in unseen conditions, indicating higher model sensitivity. Based on this, we apply Sharpness-Aware Minimization (SAM) to reduce sharpness explicitly, leading to better and more stable performance across diverse unseen test sets. Furthermore, correlation analysis confirms a statistically significant relationship between sharpness and generalization in most test settings. These findings suggest that sharpness can serve as a theoretical indicator for generalization in SDD and that sharpness-aware training offers a promising strategy for improving robustness.