The proliferation of powerful Text-to-Video (T2V) models, trained on massive web-scale datasets, raises urgent concerns about copyright and privacy violations. Membership inference attacks (MIAs) provide a principled tool for auditing such risks, yet existing techniques - designed for static data like images or text - fail to capture the spatio-temporal complexities of video generation. In particular, they overlook the sparsity of memorization signals in keyframes and the instability introduced by stochastic temporal dynamics. In this paper, we conduct the first systematic study of MIAs against T2V models and introduce a novel framework VidLeaks, which probes sparse-temporal memorization through two complementary signals: 1) Spatial Reconstruction Fidelity (SRF), using a Top-K similarity to amplify spatial memorization signals from sparsely memorized keyframes, and 2) Temporal Generative Stability (TGS), which measures semantic consistency across multiple queries to capture temporal leakage. We evaluate VidLeaks under three progressively restrictive black-box settings - supervised, reference-based, and query-only. Experiments on three representative T2V models reveal severe vulnerabilities: VidLeaks achieves AUC of 82.92% on AnimateDiff and 97.01% on InstructVideo even in the strict query-only setting, posing a realistic and exploitable privacy risk. Our work provides the first concrete evidence that T2V models leak substantial membership information through both sparse and temporal memorization, establishing a foundation for auditing video generation systems and motivating the development of new defenses. Code is available at: https://zenodo.org/records/17972831.

翻译：随着基于大规模网络数据集训练的强文本到视频（T2V）模型的激增，版权与隐私侵犯问题引发了紧迫担忧。成员推断攻击（MIAs）为审计此类风险提供了一种原则性工具，然而现有技术——专为图像或文本等静态数据设计——未能捕捉视频生成中的时空复杂性。具体而言，它们忽视了关键帧中记忆信号的稀疏性以及随机时间动态引入的不稳定性。本文首次系统性地研究了针对T2V模型的MIAs，并提出了一种新颖框架VidLeaks，该框架通过两种互补信号探测稀疏-时间记忆：1）空间重建保真度（SRF），利用Top-K相似性放大来自稀疏记忆关键帧的空间记忆信号；2）时间生成稳定性（TGS），通过测量多次查询间的语义一致性以捕捉时间泄露。我们在三种逐步严格的黑盒设置——监督式、基于参考和仅查询——下评估VidLeaks。在三种代表性T2V模型上的实验揭示了严重漏洞：即使在严格的仅查询设置下，VidLeaks在AnimateDiff上实现了82.92%的AUC，在InstructVideo上达到97.01%，构成了现实且可利用的隐私风险。我们的工作首次提供了具体证据，表明T2V模型通过稀疏记忆和时间记忆泄露了大量成员信息，为审计视频生成系统奠定了基础，并推动了新防御机制的开发。代码发布于：https://zenodo.org/records/17972831。