arXiv AI

Large Audio Language Models for Spoofing-Aware Speaker Verification

arXiv:2607. 14753v1 Announce Type: cross Abstract: Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV).

arXiv AI

Sep 12

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems

The paper introduces the Spectral Masking and Interpolation Attack (SMIA), a black‑box adversarial technique that subtly alters inaudible frequency regions of AI‑generated audio to fool voice authentication systems and their countermeasures. Experiments show SMIA achieves at least 82% success against combined verification and countermeasure systems, 97.5% against standalone speaker verification, and 100% against countermeasures, revealing a critical security gap. The authors argue that current static defenses are inadequate and call for dynamic, context‑aware defenses that can adapt to evolving threats.

By Kamel Kamel, Hridoy Sankar Dutta, Keshav Sood, Sunil Aryal

multimodal benchmarks safety

arXiv AI

Jun 10

Linguistically Augmented Audio Speech Data (LinguAS)

arXiv:2606. 10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve.

By Ashley R. Keaton, Zahra Khanjani, Christine Mallinson, Vandana P. Janeja

arXiv AI

Sep 12

A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems

The paper reviews how voice authentication has evolved from handcrafted acoustic features to deep learning speaker embeddings, expanding its use in finance, smart devices, and law enforcement. It surveys modern threats—including data poisoning, adversarial, deepfake, and adversarial spoofing attacks—tracing their development alongside technological advances. For each attack type, the authors summarize methods, datasets, performance, and limitations, and organize the literature using accepted taxonomies to highlight emerging risks and open challenges.

By Kamel Kamel, Keshav Sood, Hridoy Sankar Dutta, Sunil Aryal

rag safety

arXiv AI

Jun 18

Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation

arXiv:2603. 10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity.

By Thomas Thebaud, Yuzhe Wang, Laureano Moro-Velazquez, Jesus Villalba-Lopez, Najim Dehak

llms rag fine-tuning benchmarks

arXiv AI

Sep 1

HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding

arXiv:2608.29120v1 Announce Type: cross Abstract: Speech Language Models (SLMs) are increasingly deployed in multi-speaker environments, yet their ability to attribute speech to the correct speaker a...

By Dongwook Lee, Sangkwon Park, Eunwoo Song, Che Hyun Lee, Youngho Cho, Junho Kim, June Young Yi, Heeseung Kim, Sungroh Yoon

llms benchmarks

arXiv AI

Jul 8

What Counts as Real? Speech Restoration and Voice Quality Conversion Pose New Challenges to Deepfake Detection

arXiv:2603. 14033v2 Announce Type: replace-cross Abstract: Audio anti-spoofing systems are typically trained to assign one authenticity label to an entire speech utterance.

By Shree Harsha Bokkahalli Satish, Harm Lameris, Joakim Gustafson, \'Eva Sz\'ekely

fine-tuning

arXiv AI

Jun 30

Proteus: Automated Adversarial Robustness Testing for Audio Deepfake Detectors

arXiv:2606. 29544v1 Announce Type: cross Abstract: We present Proteus, a framework developed at Resemble AI for automated robustness testing of our audio deepfake detection system.

By Nicolas M. M\"uller, Aditya Tirumala Bukkapatnam, Zohaib Ahmed

agents reinforcement-learning safety

arXiv AI

Jun 16

Discrete optimal transport is a strong audio adversarial attack

arXiv:2509. 14959v3 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and anti-spoofing countermeasure (CM) systems.

By Anton Selitskiy, Akib Shahriyar, Jishnuraj Prakasan

rag fine-tuning safety

arXiv Machine Learning

Jul 7

An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures

arXiv:2607. 03150v1 Announce Type: cross Abstract: While deepfake audio detection systems achieve high performance in controlled benchmarks, their reliability often diminishes in the wild.

By Santiago Rubio, Pilar Bello, Dayana Ribas, Antonio Miguel, Eduardo Lleida, Alfonso Ortega

benchmarks

arXiv Machine Learning

Jun 9

Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

arXiv:2606. 08678v1 Announce Type: cross Abstract: Sophisticated generative speech technology can undermined the reliability of voice biometrics.

By Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans

robotics safety

arXiv AI

Sep 15

CRAF: Cross-View Residual-Aware Fusion for Deepfake Speech Detection

arXiv:2609.13842v1 Announce Type: cross Abstract: Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing a...

By Minh-Xuan Phan, Khalid Zaman, Candy Olivia Mawalim, Masashi Unoki

llms

arXiv AI

Jun 16

Robust Spoofed Speech Detection via Temporal Pyramid Modeling

arXiv:2606. 16837v1 Announce Type: cross Abstract: Spoofed speech detection is increasingly challenged by realistic synthesis, voice conversion, and replay attacks, with cross-dataset generalization remaining a major limitation.

By Mahtab Masoudi Nezhad, Nima Karimian

benchmarks

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