arXiv subjects

Tongqing Zhai

Publications and source records attributed to Tongqing Zhai.

4 recordsLinked to original sources

arXiv2026

CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking

Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such infringement is difficult to detect or deter. Dataset ownership verification (DOV) is the mainstream countermeasure: it can watermark a dataset with backdoor attacks so that models trained on it exhibit owner-specified behaviors. However, existing DOV methods presuppose a closed label space fixed at watermarking time, whereas in open-set speaker verification the identities that a deployed model accepts are enrolled by third parties after release and are never observed by the dataset owner. We show that straightforward adaptations fail in two characteristic modes, and accordingly distill three requirements for an effective watermark, namely identity agnosticism, coverage, and fidelity, together with an intrinsic tension between the latter two. Our clustering-based backdoor watermark (CBW) resolves this tension by partitioning training speakers into clusters by feature similarity and implanting a distinct trigger for each cluster, so that each trigger covers one region of the speaker embedding space while the trigger set is designed to jointly cover it. We further develop paired hypothesis tests for ownership verification under both the similarity-available and the decision-only black-box settings at the 1-to-1 and 1-to-$N$ enrollment scales, and theoretically characterize when the audit succeeds, including an exact small-sample certificate and the effect of the enrollment size. Extensive experiments on benchmark datasets and representative models verify the effectiveness of our CBW, its resistance to watermark-removal attacks, and its transferability across model structures. Code is at https://github.com/Radiant0726/CBW/tree/master.

cs.CR↗

arXiv2021

Backdoor Attack in the Physical World

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of infected models will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger. Currently, most existing backdoor attacks adopted the setting of static trigger, $i.e.,$ triggers across the training and testing images follow the same appearance and are located in the same area. In this paper, we revisit this attack paradigm by analyzing trigger characteristics. We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training. As such, those attacks are far less effective in the physical world, where the location and appearance of the trigger in the digitized image may be different from that of the one used for training. Moreover, we also discuss how to alleviate such vulnerability. We hope that this work could inspire more explorations on backdoor properties, to help the design of more advanced backdoor attack and defense methods.

cs.CR↗

arXiv2021

Backdoor Attack against Speaker Verification

Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount of data, therefore users usually need to adopt third-party data ($e.g.$, data from the Internet or third-party data company). This raises the question of whether adopting untrusted third-party data can pose a security threat. In this paper, we demonstrate that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data. Specifically, we design a clustering-based attack scheme where poisoned samples from different clusters will contain different triggers ($i.e.$, pre-defined utterances), based on our understanding of verification tasks. The infected models behave normally on benign samples, while attacker-specified unenrolled triggers will successfully pass the verification even if the attacker has no information about the enrolled speaker. We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification. Our approach not only provides a new perspective for designing novel attacks, but also serves as a strong baseline for improving the robustness of verification methods. The code for reproducing main results is available at \url{https://github.com/zhaitongqing233/Backdoor-attack-against-speaker-verification}.

cs.CR↗

arXiv2021

Rethinking the Trigger of Backdoor Attack

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger, while it performs well on benign samples. Currently, most of existing backdoor attacks adopted the setting of \emph{static} trigger, $i.e.,$ triggers across the training and testing images follow the same appearance and are located in the same area. In this paper, we revisit this attack paradigm by analyzing the characteristics of the static trigger. We demonstrate that such an attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training. We further explore how to utilize this property for backdoor defense, and discuss how to alleviate such vulnerability of existing attacks.

cs.CR↗