Yixian Yang

Zhisheng Zhang, Derui Wang, Qianyi Yang et al.

Speech synthesis technology has brought great convenience, while the widespread usage of realistic deepfake audio has triggered hazards. Malicious adversaries may unauthorizedly collect victims' speeches and clone a similar voice for illegal exploitation (\textit{e.g.}, telecom fraud). However, the existing defense methods cannot effectively prevent deepfake exploitation and are vulnerable to robust training techniques. Therefore, a more effective and robust data protection method is urgently needed. In response, we propose a defensive framework, \textit{\textbf{SafeSpeech}}, which protects the users' audio before uploading by embedding imperceptible perturbations on original speeches to prevent high-quality synthetic speech. In SafeSpeech, we devise a robust and universal proactive protection technique, \textbf{S}peech \textbf{PE}rturbative \textbf{C}oncealment (\textbf{SPEC}), that leverages a surrogate model to generate universally applicable perturbation for generative synthetic models. Moreover, we optimize the human perception of embedded perturbation in terms of time and frequency domains. To evaluate our method comprehensively, we conduct extensive experiments across advanced models and datasets, both subjectively and objectively. Our experimental results demonstrate that SafeSpeech achieves state-of-the-art (SOTA) voice protection effectiveness and transferability and is highly robust against advanced adaptive adversaries. Moreover, SafeSpeech has real-time capability in real-world tests. The source code is available at \href{https://github.com/wxzyd123/SafeSpeech}{https://github.com/wxzyd123/SafeSpeech}.

Weifei Jin, Yuxin Cao, Junjie Su et al.

The widespread application of automatic speech recognition (ASR) supports large-scale voice surveillance, raising concerns about privacy among users. In this paper, we concentrate on using adversarial examples to mitigate unauthorized disclosure of speech privacy thwarted by potential eavesdroppers in speech communications. While audio adversarial examples have demonstrated the capability to mislead ASR models or evade ASR surveillance, they are typically constructed through time-intensive offline optimization, restricting their practicality in real-time voice communication. Recent work overcame this limitation by generating universal adversarial perturbations (UAPs) and enhancing their transferability for black-box scenarios. However, they introduced excessive noise that significantly degrades audio quality and affects human perception, thereby limiting their effectiveness in practical scenarios. To address this limitation and protect live users' speech against ASR systems, we propose a novel framework, AudioShield. Central to this framework is the concept of Transferable Universal Adversarial Perturbations in the Latent Space (LS-TUAP). By transferring the perturbations to the latent space, the audio quality is preserved to a large extent. Additionally, we propose target feature adaptation to enhance the transferability of UAPs by embedding target text features into the perturbations. Comprehensive evaluation on four commercial ASR APIs (Google, Amazon, iFlytek, and Alibaba), three voice assistants, two LLM-powered ASR and one NN-based ASR demonstrates the protection superiority of AudioShield over existing competitors, and both objective and subjective evaluations indicate that AudioShield significantly improves the audio quality. Moreover, AudioShield also shows high effectiveness in real-time end-to-end scenarios, and demonstrates strong resilience against adaptive countermeasures.

Dawei Zhao, Haipeng Peng, Lixiang Li et al.

Recently, Mun et al. analyzed Wu et al.'s authentication scheme and proposed a enhanced anonymous authentication scheme for roaming service in global mobility networks. However, through careful analysis, we find that Mun et al.'s scheme is vulnerable to impersonation attacks, off-line password guessing attacks and insider attacks, and cannot provide user friendliness, user's anonymity, proper mutual authentication and local verification. To remedy these weaknesses, in this paper we propose a novel anonymous authentication scheme for roaming service in global mobility networks. Security and performance analyses show the proposed scheme is more suitable for the low-power and resource-limited mobile devices, and is secure against various attacks and has many excellent features.

Dawei Zhao, Haipeng Peng, Shudong Li et al.

Recently, Li et al. analyzed Lee et al.'s multi-server authentication scheme and proposed a novel smart card and dynamic ID based remote user authentication scheme for multi-server environments. They claimed that their scheme can resist several kinds of attacks. However, through careful analysis, we find that Li et al.'s scheme is vulnerable to stolen smart card and offline dictionary attack, replay attack, impersonation attack and server spoofing attack. By analyzing other similar schemes, we find that the certain type of dynamic ID based multi-server authentication scheme in which only hash functions are used and no registration center participates in the authentication and session key agreement phase is hard to provide perfect efficient and secure authentication. To compensate for these shortcomings, we improve the recently proposed Liao et al.'s multi-server authentication scheme which is based on pairing and self-certified public keys, and propose a novel dynamic ID based remote user authentication scheme for multi-server environments. Liao et al.'s scheme is found vulnerable to offline dictionary attack and denial of service attack, and cannot provide user's anonymity and local password verification. However, our proposed scheme overcomes the shortcomings of Liao et al.'s scheme. Security and performance analyses show the proposed scheme is secure against various attacks and has many excellent features.