The advent of Text-to-Image generative models poses significant risks of copyright violation and deepfake generation. Since the rapid proliferation of new copyrighted works and private individuals constantly emerges, reference-based training-free content filters are essential for providing up-to-date protection without the constraints of a fixed knowledge cutoff. However, existing reference-based approaches often lack scalability when handling numerous references and require waiting for finishing image generation. To solve these problems, we propose EDGE-Shield, a scalable content filter during the denoising process that maintains practical latency while effectively blocking violative content. We leverage embedding-based matching for efficient reference comparison. Additionally, we introduce an \textit{$x$}-pred transformation that converts the model's noisy intermediate latent into the pseudo-estimated clean latent at the later stage, enhancing classification accuracy of violative content at earlier denoising stages. We conduct experiments of violative content filtering against two generative models including Z-Image-Turbo and Qwen-Image. EDGE-Shield significantly outperforms traditional reference-based methods in terms of latency; it achieves an approximate $79\%$ reduction in processing time for Z-Image-Turbo and approximate $50\%$ reduction for Qwen-Image, maintaining the filtering accuracy across different model architectures.

翻译：文本到图像生成模型的出现带来了版权侵权和深度伪造生成的重大风险。由于新的版权作品和私人个体不断涌现，基于参考的无训练内容过滤器对于提供不受固定知识截止日期限制的最新保护至关重要。然而，现有基于参考的方法在处理大量参考时往往缺乏可扩展性，并且需要等待图像生成完成。为解决这些问题，我们提出了EDGE-Shield，一种在去噪过程中保持实际延迟的同时有效拦截违禁内容的可扩展内容过滤器。我们利用基于嵌入的匹配进行高效的参考比较。此外，我们引入了一种\textit{$x$}-预测变换，将模型有噪声的中间潜在变量转换为后期阶段的伪估计干净潜在变量，从而提高早期去噪阶段对违禁内容的分类准确性。我们针对两种生成模型（包括Z-Image-Turbo和Qwen-Image）进行了违禁内容过滤实验。EDGE-Shield在延迟方面显著优于传统的基于参考方法；它在Z-Image-Turbo上实现了约79%的处理时间减少，在Qwen-Image上实现了约50%的减少，并在不同模型架构下保持了过滤准确性。