Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

• URL: http://arxiv.org/abs/2006.14563v1
• Date: Thu, 25 Jun 2020 17:06:47 GMT
• Title: Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection
• Authors: Yongqiang Dou, Haocheng Yang, Maolin Yang, Yanyan Xu and Dengfeng Ke
• Abstract summary: We argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process. We propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. With complementary features, our fusion system with only three kinds of features outperforms other systems by 22.5% for min-tDCF and 7% for EER.
• Score: 10.851348154870852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.

Related papers

• Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic [2.5182419298876857]
  Multi-class classification models can identify specific types of attacks, allowing for more targeted and effective incident responses. Recent advances suggest that generative models can assist in data augmentation, claiming to offer superior solutions for imbalanced datasets. Our experiments indicate that resampling methods for balancing training data do not reliably improve classification performance.
  arXiv  Detail & Related papers  (2024-08-28T12:44:07Z)
• Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples [33.445126880876415]
  We propose a reliable and robust spoofing detection system to filter out spoofing attacks instead of having them reach the automatic speaker verification system. A weighted additive angular margin loss is proposed to address the data imbalance issue, and different margins has been assigned to improve generalization to unseen spoofing attacks. We craft adversarial examples by adding imperceptible perturbations to spoofing speech as a data augmentation strategy, then we use an auxiliary batch normalization to guarantee that corresponding normalization statistics are performed exclusively on the adversarial examples.
  arXiv  Detail & Related papers  (2024-08-23T19:26:54Z)
• Class Imbalance in Object Detection: An Experimental Diagnosis and Study of Mitigation Strategies [0.5439020425818999]
  This study introduces a benchmarking framework utilizing the YOLOv5 single-stage detector to address the problem of foreground-foreground class imbalance. We scrutinized three established techniques: sampling, loss weighing, and data augmentation. Our comparative analysis reveals that sampling and loss reweighing methods, while shown to be beneficial in two-stage detector settings, do not translate as effectively in improving YOLOv5's performance.
  arXiv  Detail & Related papers  (2024-03-11T19:06:04Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Scale-Equivalent Distillation for Semi-Supervised Object Detection [57.59525453301374]
  Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. We analyze the challenges these methods meet with the empirical experiment results. We introduce a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
  arXiv  Detail & Related papers  (2022-03-23T07:33:37Z)
• Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery [2.580765958706854]
  Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. Traditional Fault Detection and Diagnosis (FDD) frameworks get poor performances when dealing with real-world circumstances. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal-based FDD system.
  arXiv  Detail & Related papers  (2022-02-09T01:09:59Z)
• Impact of Channel Variation on One-Class Learning for Spoof Detection [5.549602650463701]
  Spoofing detection increases the reliability of the ASV system but degrades significantly due to channel variation. "Which data-feeding strategy is optimal for MCT?" is not known in the case of spoof detection. This study highlights the relevance of the deemed-of-low-importance process of data-feeding and mini-batching to raise awareness of the need to refine it for better performance.
  arXiv  Detail & Related papers  (2021-09-30T07:56:16Z)
• Continual Learning for Fake Audio Detection [62.54860236190694]
  This paper proposes detecting fake without forgetting, a continual-learning-based method, to make the model learn new spoofing attacks incrementally. Experiments are conducted on the ASVspoof 2019 dataset.
  arXiv  Detail & Related papers  (2021-04-15T07:57:05Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)
• Adaptive Adversarial Logits Pairing [65.51670200266913]
  An adversarial training solution Adversarial Logits Pairing (ALP) tends to rely on fewer high-contribution features compared with vulnerable ones. Motivated by these observations, we design an Adaptive Adversarial Logits Pairing (AALP) solution by modifying the training process and training target of ALP. AALP consists of an adaptive feature optimization module with Guided Dropout to systematically pursue fewer high-contribution features.
  arXiv  Detail & Related papers  (2020-05-25T03:12:20Z)

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.