Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection

• URL: http://arxiv.org/abs/2109.02051v1
• Date: Sun, 5 Sep 2021 12:10:16 GMT
• Title: Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection
• Authors: Amir Mohammad Rostami, Mohammad Mehdi Homayounpour, Ahmad Nickabadi
• Abstract summary: Models currently deployed for the task of Automatic Speaker Verification are, at their best, devoid of suitable degrees of generalization to unseen attacks. The present study proposes the Efficient Attention Branch Network (EABN) modular architecture with a combined loss function to address the generalization problem.
• Score: 7.219077740523682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks. As evidenced by the latest ASVspoof 2019 countermeasure challenge, models currently deployed for the task of ASV are, at their best, devoid of suitable degrees of generalization to unseen attacks. Upon further investigation of the proposed methods, it appears that a broader three-tiered view of the proposed systems. comprised of the classifier, feature extraction phase, and model loss function, may to some extent lessen the problem. Accordingly, the present study proposes the Efficient Attention Branch Network (EABN) modular architecture with a combined loss function to address the generalization problem...

Related papers

• Multi-agent Reinforcement Learning-based Network Intrusion Detection System [3.4636217357968904]
  Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. We propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns.
  arXiv  Detail & Related papers  (2024-07-08T09:18:59Z)
• Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study [2.5325901958283126]
  This paper proposes a novel 3-stage intrusion detection system inspired by a simplified version of the Lockheed Martin cyber kill chain. The proposed approach consists of three models, each responsible for detecting a group of attacks with common characteristics. Using the ToN IoT dataset, we achieved an average of 94% F1-Score among different stages, outperforming the benchmark approaches.
  arXiv  Detail & Related papers  (2024-04-28T22:11:24Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Efficient Network Representation for GNN-based Intrusion Detection [2.321323878201932]
  The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages. We propose a novel network representation as a graph of flows that aims to provide relevant topological information for the intrusion detection task. We present a Graph Neural Network (GNN) based framework responsible for exploiting the proposed graph structure.
  arXiv  Detail & Related papers  (2023-09-11T16:10:12Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning [43.519717601587864]
  This study introduces a simple attention module to infer 3-dim attention weights for the feature map in a convolutional layer. We propose a joint optimization approach based on the weighted additive angular margin loss for binary classification. Our proposed approach delivers a competitive result with a pooled EER of 0.99% and min t-DCF of 0.0289.
  arXiv  Detail & Related papers  (2022-11-17T21:25:29Z)
• Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks [67.7648985513978]
  Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. We present a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism.
  arXiv  Detail & Related papers  (2021-07-19T12:27:40Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Selective and Features based Adversarial Example Detection [12.443388374869745]
  Security-sensitive applications that relay on Deep Neural Networks (DNNs) are vulnerable to small perturbations crafted to generate Adversarial Examples (AEs) We propose a novel unsupervised detection mechanism that uses the selective prediction, processing model layers outputs, and knowledge transfer concepts in a multi-task learning setting. Experimental results show that the proposed approach achieves comparable results to the state-of-the-art methods against tested attacks in white box scenario and better results in black and gray boxes scenarios.
  arXiv  Detail & Related papers  (2021-03-09T11:06:15Z)
• Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification [78.51092318750102]
  This work proposes to defend ASV systems against adversarial attacks with a separate detection network. A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples.
  arXiv  Detail & Related papers  (2020-06-11T04:31:56Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.