STAA-Net：一种用于语音情感识别的稀疏可迁移对抗攻击
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition
AI总结：
针对语音情感识别模型，提出基于生成器的STAA-Net方法，高效生成稀疏且可迁移的对抗样本，并在DEMoS和IEMOCAP数据集上验证其有效性。
摘要：
语音包含丰富的人类情感信息，语音情感识别（SER）一直是人机交互领域的重要课题。SER模型的鲁棒性至关重要，尤其是在隐私敏感和可靠性要求高的领域，如私人医疗保健。近年来，深度神经网络在音频领域对抗攻击的脆弱性已成为一个热门研究领域。然而，先前在音频领域的对抗攻击工作主要依赖于基于梯度的迭代技术，这些技术耗时且容易过度拟合特定的威胁模型。此外，在音频领域，具有更好隐蔽性潜力的稀疏扰动的探索仍然有限。为了解决这些挑战，我们提出了一种基于生成器的攻击方法，以端到端且高效的方式生成稀疏且可迁移的对抗样本，以欺骗SER模型。我们在两个广泛使用的SER数据集上评估了我们的方法，即语音诱发情绪数据库（DEMoS）和交互式情感二元动作捕捉（IEMOCAP），并证明了其能够以高效的方式生成成功的稀疏对抗样本。此外，我们生成的对抗样本表现出模型无关的可迁移性，能够对先进的受害者模型进行有效的对抗攻击。
英文摘要：
Speech contains rich information on the emotions of humans, and Speech Emotion Recognition (SER) has been an important topic in the area of human-computer interaction. The robustness of SER models is crucial, particularly in privacy-sensitive and reliability-demanding domains like private healthcare. Recently, the vulnerability of deep neural networks in the audio domain to adversarial attacks has become a popular area of research. However, prior works on adversarial attacks in the audio domain primarily rely on iterative gradient-based techniques, which are time-consuming and prone to overfitting the specific threat model. Furthermore, the exploration of sparse perturbations, which have the potential for better stealthiness, remains limited in the audio domain. To address these challenges, we propose a generator-based attack method to generate sparse and transferable adversarial examples to deceive SER models in an end-to-end and efficient manner. We evaluate our method on two widely-used SER datasets, Database of Elicited Mood in Speech (DEMoS) and Interactive Emotional dyadic MOtion CAPture (IEMOCAP), and demonstrate its ability to generate successful sparse adversarial examples in an efficient manner. Moreover, our generated adversarial examples exhibit model-agnostic transferability, enabling effective adversarial attacks on advanced victim models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
