Diffusion-based text-to-image (T2I) models are increasingly used for visual content creation, making their generation capability a valuable intellectual property asset. However, this capability is vulnerable to black-box output-based distillation, where an adversary queries the service, collects prompt-image pairs, and trains an unauthorized substitute model that mimics its generation behavior. Existing perturbation-based defenses apply sample-wise optimization to make generated images disruptive to unauthorized training, but introduce substantial computation and latency that reduce the usability of online T2I services. A natural solution is to integrate defensive perturbations into the VAE decoder, allowing the protected model to generate defended images directly without online sample-wise optimization. However, existing sample-wise objectives struggle to transfer to the shared decoder setting. We empirically find that a defensive shared decoder induces a substantially smaller latent shift than sample-wise optimization, suggesting that objective reachability matters more than destructiveness in this setting. To overcome this limitation, we propose RAPID, a self-referenced latent maximization framework that removes external dependencies and encourages the same model update to induce consistently disruptive effects across training samples, thereby improving reachability. We further introduce reconstruction-guided color regularization that blocks the latent shortcut and reinforces visual disruption. Extensive experiments on four T2I models and four datasets, with comparisons against five representative baselines, show that RAPID consistently degrades substitute-model generation quality while preserving service visual fidelity. Our work establishes a paradigm for real-time protection against unauthorized distillation in deployed T2I systems.
Zi-Han Wang, Bo-Heng Li, Rui Zhang et al.· 0 citations
Retrieval-Augmented Generation (RAG) improves the factuality of large language models with external knowledge, yet conflicting evidence remains a fundamental challenge in dynamic and adversarial environments. Existing approaches often treat conflicts as static inconsistencies and select more reliable knowledge, overlooking that the same conflict may arise from legitimate knowledge evolution, malicious manipulation, or unresolved uncertainty. We formulate conflict origin attribution as a new problem in RAG: identifying which explanation of conflicting evidence is supported by observable context rather than simply which fact should be trusted. We propose EvoTrustRAG, a training-free framework for evolution-aware conflict attribution and evidence handling before answer generation. EvoTrustRAG represents span-grounded retrieved facts as a conflict evidence graph, evaluates grounded evolution and directional intervention hypotheses using temporal relations, support structure, and auxiliary consistency, and projects local decisions onto a globally consistent explanation of each conflict group. The attribution determines whether earlier and later states are preserved as temporal knowledge, an intervention candidate is separated from the primary context, or an unresolved conflict remains visible to the generator. Unlike provenance-based approaches focused on post-hoc analysis, EvoTrustRAG determines during inference whether conflicting evidence follows plausible knowledge evolution, exhibits intervention-like support, or cannot be reliably attributed. Experiments show that EvoTrustRAG achieves 81.4% average accuracy on benchmark-native conflict settings, improves attribution macro-F1 from 72.2% to 79.1% over the strongest baseline, and reduces the error rate under the strongest coordinated attack from 31.2% to 16.0%.
Xiao-Rui Nie, Hongwei Li, Shenghao Wu et al.· 0 citations
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