Papers Audio Deepfake Detection
“Audio Deepfake Detection” 태그가 달린 논문 120편 · 필터 해제
Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection
Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals. We hypothesise that these constraints manifest as structured trajectory dynamics in the latent s…
Audio Deepfake DetectionSelf-Supervised LearningAudio-Anchored Fusion of Multi-Ratio DiT Reconstruction Residuals for Cross-Domain Audio Deepfake Detection
Audio deepfake detectors often degrade when generators, corpora, or recording conditions change. We use a Diffusion Transformer (DiT), trained only on bona fide speech, as a frozen reconstruction probe. Reconstructions a…
Audio Deepfake DetectionExplainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Prediction
The rapid advancement of synthetic speech generation methods has made audio deepfake detection a critical challenge in multimedia forensics. While recent approaches achieve high detection accuracy, they typically rely on…
Audio Deepfake DetectionSynSFX: Multi-Model Sound Effects Synthesis Dataset for Deepfake Detection and Evaluation
While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects. Existing environmental audio datasets such as EnvSDD provide important initial r…
Audio Deepfake DetectionDetecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin
Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers. The latter need reliable tools to verify the authenticity of their sources, while at the same time keeping the…
Audio Deepfake DetectionProteus: Automated Adversarial Robustness Testing for Audio Deepfake Detectors
We present Proteus, a framework developed at Resemble AI for automated robustness testing of our audio deepfake detection system. Given a detector, Proteus systematically searches over sequences of everyday audio transfo…
Audio Deepfake DetectionAdversarial RobustnessThe Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection
Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedicated techniques such as AudioSeal, or de…
Audio Deepfake DetectionFlowFake: Liquid Networks for Audio Deepfake Detection
Audio deepfakes generated by neural text-to-speech and voice-cloning systems threaten speaker verification and public discourse at scale. The core challenge is cross-dataset generalization: detectors trained on one synth…
Audio Deepfake DetectionSpeaker VerificationDual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection
Audio deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts, known as implicit identity leakage. Existing methods address this but incur arch…
Audio Deepfake DetectionThe Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions
This paper investigates the fragility of post-hoc explanation methods in audio deepfake detection. While previous work on explanation manipulation focused on images using standard $L_p$ metrics, we introduce a psychoacou…
Audio Deepfake DetectionFoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors
Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models. Evaluating and strengthening ADD models requires developing datasets that span the space of generated au…
Audio Deepfake DetectionRed TeamingAudio Deepfake Detection with Half-Truth Localisation Using Cross-Attentive Feature Fusion
Audio deepfake detection is well-studied as a binary problem, but partially manipulated speech, where a short synthesised segment is spliced into an otherwise genuine utterance, poses a harder and more realistic threat. …
Audio Deepfake DetectionTowards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains critically underexamined. Prior work measures gender disparity but doe…
Audio Deepfake DetectionQuantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features
Quantum machine learning has emerged as a promising tool for pattern recognition, yet many audio-focused approaches still treat spectrograms as generic images and do not explicitly exploit their time-frequency structure.…
Quantum Machine LearningAudio Deepfake DetectionToward Fine-Grained Speech Inpainting Forensics:A Dataset, Method, and Metric for Multi-Region Tampering Localization
Recent advances in voice cloning and text-to-speech synthesis have made partial speech manipulation - where an adversary replaces a few words within an utterance to alter its meaning while preserving the speaker's identi…
Text-To-Speech SynthesisAudio Deepfake DetectionBinary ClassificationPhoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake detection. Existing approaches treat speech as a homogeneous signal a…
Audio Deepfake DetectionVoice ConversionSimilarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection
Supervised contrastive learning (SupCon) is widely used to shape representations, but has seen limited targeted study for audio deepfake detection. Existing work typically combines contrastive terms with broader pipeline…
Audio Deepfake DetectionContrastive LearningICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection
Audio deepfakes pose a significant security threat, yet current state-of-the-art (SOTA) detection systems do not generalize well to realistic in-the-wild deepfakes. We introduce a novel \textbf{I}n-\textbf{C}ontext \text…
Audio Deepfake DetectionQuantum Vision Theory Applied to Audio Classification for Deepfake Speech Detection
We propose Quantum Vision (QV) theory as a new perspective for deep learning-based audio classification, applied to deepfake speech detection. Inspired by particle-wave duality in quantum physics, QV theory is based on t…
Audio Deepfake DetectionAudio ClassificationImage ClassificationAT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
The rapid advancement of Audio Large Language Models (ALLMs) has enabled cost-effective, high-fidelity generation and manipulation of both speech and non-speech audio, including sound effects, singing voices, and music. …
Audio Deepfake Detection