小心脚下：通过影子时间步嵌入在扩散模型中注入信息
Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
AI总结：
研究探讨了扩散模型中时间步嵌入的潜在信息注入风险，提出影子时间步嵌入机制，揭示时间步嵌入作为侧信道的潜力，推动对抗生成建模的新方向。
摘要：
扩散模型已成为现代生成系统的基础，大多数研究主要集中在提高生成效率和输出质量上。时间步嵌入是扩散流程中的关键部分，为去噪网络提供时间条件信号，使其能够在不同噪声水平下调整预测。尽管它们有潜力包含大量信息，但时间步嵌入在当前研究中仍被忽视，尤其是在安全风险和可靠溯源方面。为填补这一空白，我们引入了影子时间步嵌入（STE），一种新的机制，研究了未被利用的时间空间以向扩散模型注入恶意信息。特别地，当我们聚焦于时间步嵌入空间时，发现不同时间步展现出不同的表示能力，可以编码侧信道信息。此外，此类编码信息可通过调度器接口用于攻击和防御。我们对时间步嵌入作为位置编码映射进行了理论分析，并推导出一个互相关性评估，解释了不同时刻区间的可分离性。我们的发现揭示了扩散模型的时间步作为携带专用信息的强大侧信道，通过理解时间维度推动对抗生成建模的新方向。
英文摘要：
Diffusion models have become the foundation of modern generative systems, with most research focusing primarily on improving generation efficiency and output quality. The timestep embedding component is a crucial part of the diffusion pipeline, which provides a temporal conditioning signal to the denoising network, enabling it to adapt its predictions across different noise levels throughout the process. Despite their potential to contain substantial information, timestep embeddings remain underexplored in current research, especially for security risks and reliable provenance. To fill this gap, we introduce Shadow Timestep Embedding (STE), a novel mechanism that investigates the underutilized temporal space for malicious information injection into diffusion models. In particular, when zooming in on the timestep embedding space, we find that different timesteps exhibit distinct representational capabilities that can encode side-channel information. Moreover, such encoded information can be utilized for attack and defense purposes through the scheduler interface. We present a theoretical analysis of timestep embeddings as position-encoding mappings and derive a mutual coherence evaluation that explains the separability of disjoint timestep intervals. Our findings reveal the diffusion model's timestep as a powerful side channel for carrying dedicated information, motivating new directions for adversarial generative modeling by understanding the temporal dimension.
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