We introduce the coded-smoothing module, which can be seamlessly integrated into standard training pipelines, both supervised and unsupervised, to regularize learning and improve generalization with minimal computational overhead. In addition, it can be incorporated into the inference pipeline to randomize the model and enhance robustness against adversarial perturbations. The design of coded-smoothing is inspired by general coded computing, a paradigm originally developed to mitigate straggler and adversarial failures in distributed computing by processing linear combinations of the data rather than the raw inputs. Building on this principle, we adapt coded computing to machine learning by designing an efficient and effective regularization mechanism that encourages smoother representations and more generalizable solutions. Extensive experiments on both supervised and unsupervised tasks demonstrate that coded-smoothing consistently improves generalization and achieves state-of-the-art robustness against gradient-based adversarial attacks.

Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need for robust countermeasures more than ever. Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data. This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations. Extensive experiments have been performed to find suitable non-semantic features using TRILL and TRILLsson models. The results indicate that the proposed method achieves comparable performance on the in-domain test set while significantly outperforming state-of-the-art approaches on out-of-domain test sets. Notably, it demonstrates superior generalization on public-domain data, surpassing methods based on hand-crafted features, semantic embeddings, and end-to-end architectures.