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
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
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
arXiv:2610.16049v1 Announce Type: cross
Abstract: Existing audio deepfake detection (ADD) methods frequently exhibit limited generalization, as fixed bottleneck architectures fail to adapt to the het...
By Jianqiao Cui, Haoxian Zhu, Hanbiao Meng, Yanghao Zhang
The paper introduces ROGUE, a framework that builds robust audio deepfake detection workflows by combining multiple detection tools. ROGUE treats workflow creation as a sequential decision problem and uses a dual-agent system: a perturbation agent generates audio distortions while a policy agent selects and executes detection tools that are resilient to those perturbations. Experiments on several datasets and real-world corruptions show that ROGUE consistently outperforms strong baselines in robustness and generalization, demonstrating the value of adversarially optimized workflow generation for reliable deployment.
By Xiang Li, Pin-Yu Chen, Wenqi Wei
arXiv:2606. 30780v1 Announce Type: cross Abstract: Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers.
By Octavian Pascu, Dan Oneata, Horia Cucu, Nicolas M. Muller