Spoof
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
MultiAPI Spoof: A Multi-API Dataset and Local-Attention Network for Speech Anti-spoofing Detection
arXiv:2512.07352v4 Announce Type: replace Abstract: Existing speech anti-spoofing benchmarks rely on a narrow set of public models, creating a substantial gap from real-world scenarios in which commercial systems employ diverse, often proprietary APIs. To address this issue, we introduce MultiAPI Spoof, a multi-API audio anti-spoofing dataset comprising about 230 hours of synthetic speech generated by 30 distinct APIs, including commercial services, open-source models, and online platforms....
Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck
Announce Type: new Abstract: Sophisticated generative speech technology can undermined the reliability of voice biometrics. While spoofing detection systems excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. In this paper, we show that such issues could be caused by speaker bias, where models learn individual voice traits rather than markers of manipulation or generation.
Northern Ireland cops issue PSA after official phone number spoofed by scammers
The Police Service of Northern Ireland (PSNI) is warning the public to be wary of scammers spoofing its switchboard number in an attempt to profit by calling marks from a "trustworthy" number. A member of the public reported an attempted scam on Monday afternoon. A phone call came in from what appeared to be the PSNI’s switchboard number, and the caller pretended to be a member of the force inquiring about a case in which the recipient was involved.
Self-supervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing
arXiv:2503.22929v3 Announce Type: replace Abstract: Face anti-spoofing (FAS) techniques aim to enhance the security of facial identity authentication by distinguishing authentic live faces from deceptive attempts. While two-class FAS methods risk overfitting to training attacks to achieve better performance, one-class FAS approaches handle unseen attacks well but are less robust to domain information entangled within the liveness features. To address this, we propose an Unsupervised Feature...
A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis
arXiv:2606.08669v1 Announce Type: new Abstract: Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To investigate these unpredictable fluctuations, we conduct a comprehensive benchmark of four self-supervised learning feature extractors paired with four back-end classifiers. We compare the hierarchical local feature extraction of ResNet with the global sequence and relational modeling of attention...
A Training-Efficient Transformer-Based Anti-Spoofing Network for Logical Access in ASVspoof 5
arXiv:2606.02980v1 Announce Type: new Abstract: Synthetic and manipulated speech can reduce the reliability of automatic speaker verification systems, so anti-spoofing methods need to be both accurate and efficient in training and inference. This paper focuses on the ASVspoof 5 Track 1 closed condition, where standard cross-entropy training may not give enough attention to hard trials and is not directly aligned with ranking- and threshold-based evaluation metrics. We propose TFPARN, a...
Exploring the Scale and Diversity of Speech Anti-spoofing Datasets: Experiments and Analysis
arXiv:2606.08038v1 Announce Type: new Abstract: The scale of speech anti-spoofing datasets has grown exponentially over the past decade, driven by the assumption that larger data leads to better performance. However, it remains unclear whether indiscriminate scaling commensurately improves model generalization. This study challenges the "scale-first" paradigm by decoupling the impacts of training data scale versus diversity.
Scary Movie review – spoof comedy returns but maybe it should have stayed in the 2000s
Successful jokes are thin on the ground in the musty sixth installment of the once-popular parody franchise, taking aim at everything from Scream to SinnersThe Scary Movie series has always depended on timing. Not necessarily in its gagcraft, which has oscillated between occasional sharp jabs and many beyond-broad blows, but in its position on the release schedule. This was especially true of the first installment, which arrived in theaters just a few months after the 2000 release of Scream...
Scary Movie review – spoof comedy returns but maybe it should have stayed in the 2000s
Successful jokes are thin on the ground in the musty sixth installment of the once-popular parody franchise, taking aim at everything from Scream to SinnersThe Scary Movie series has always depended on timing. Not necessarily in its gagcraft, which has oscillated between occasional sharp jabs and many beyond-broad blows, but in its position on the release schedule. This was especially true of the first installment, which arrived in theaters just a few months after the 2000 release of Scream...
Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
Announce Type: replace Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation. However, global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL approaches typically improve resilience against specific attack models but often fail to generalize to attacks not encountered during training.