Our design policy successfully achieves amortized intervention design on the distribution of the training environment while also generalizing well to distribution shifts in test-time design environments.

Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities.

This protocol is often regarded as a "golden compass" for navigating SoT A detectors. But can these stand-out "winners" be

However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

We propose a probabilistic perspective on adversarial examples, allowing us to embed subjective understanding of semantics as a distribution into the process of generating adversarial examples, in a principled manner.

But where do such hallucinations come from?