arXiv subjects

Raphael Phan

Publications and source records attributed to Raphael Phan.

3 recordsLinked to original sources

arXiv2026

On the Robustness of Audio Deepfake Detection under Audio Watermarking

Recent advances in generative audio models have enabled highly realistic synthetic speech, increasing the importance of reliable audio deepfake detection (ADD) systems. While prior studies have primarily focused on adversarially optimized perturbations, the robustness of ADD systems under realistic signal transformations remains insufficiently understood. In this work, we investigate the impact of audio watermarking on ADD systems by treating watermarking as a structured, non-adversarial perturbation rather than a conventional attack mechanism. Using a watermark-based evaluation framework built upon WavMark, we evaluate multiple self-supervised learning (SSL), Convolutional Neural Network (CNN) and Graph Neural Netrowk (GNN)-based ADD models across several benchmark datasets. Beyond conventional detection metrics, we further analyze watermark-induced representation shifts using Fr\'echet Distance, cosine similarity, and L2 distance in the embedding space. Experimental results reveal a strong dataset-dependent behavior: watermarking causes substantial performance degradation on ASVspoof 2021 LA and DF, while exhibiting limited impact on ASVspoof 2024, FoR, and ITW. Moreover, large embedding-space shifts are strongly associated with severe detection degradation, suggesting that watermark-induced perturbations can substantially alter the feature representations relied upon by current ADD systems. These findings demonstrate that benign signal transformations designed for content protection can expose previously overlooked robustness vulnerabilities in audio deepfake detection systems. Our code is available at https://github.com/ziqian0925/wm-ADD-robustness.git

cs.SD↗

arXiv2025

Denoising via Repainting: an image denoising method using layer wise medical image repainting

Medical image denoising is essential for improving the reliability of clinical diagnosis and guiding subsequent image-based tasks. In this paper, we propose a multi-scale approach that integrates anisotropic Gaussian filtering with progressive Bezier-path redrawing. Our method constructs a scale-space pyramid to mitigate noise while preserving critical structural details. Starting at the coarsest scale, we segment partially denoised images into coherent components and redraw each using a parametric Bezier path with representative color. Through iterative refinements at finer scales, small and intricate structures are accurately reconstructed, while large homogeneous regions remain robustly smoothed. We employ both mean square error and self-intersection constraints to maintain shape coherence during path optimization. Empirical results on multiple MRI datasets demonstrate consistent improvements in PSNR and SSIM over competing methods. This coarse-to-fine framework offers a robust, data-efficient solution for cross-domain denoising, reinforcing its potential clinical utility and versatility. Future work extends this technique to three-dimensional data.

eess.IV↗

arXiv2024

Toward end-to-end interpretable convolutional neural networks for waveform signals

This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models, presenting advancements in efficiency and explainability. By benchmarking experiments on three standard speech emotion recognition datasets with five-fold cross-validation, our framework outperforms Mel spectrogram features by up to seven percent. It can potentially replace the Mel-Frequency Cepstral Coefficients (MFCC) while remaining lightweight. Furthermore, we demonstrate the efficiency and interpretability of the front-end layer using the PhysioNet Heart Sound Database, illustrating its ability to handle and capture intricate long waveform patterns. Our contributions offer a portable solution for building efficient and interpretable models for raw waveform data.

cs.SD↗