NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusion

The preprint operates within the deep learning paradigm, specifically focusing on adversarial attacks and defenses within the image classification domain. It addresses the sub-paradigm of denoising diffusion probabilistic models for image generation and their application to adversarial sample creation.

The preprint presents a novel method (NatADiff) for generating natural adversarial examples using diffusion models. While it introduces a new technique and explores its efficacy, it does not challenge the fundamental assumptions or methods of the existing deep learning paradigm or the sub-paradigm of diffusion models. It primarily builds upon existing techniques like classifier guidance and time-travel sampling, refining them and combining them with a new approach called “adversarial boundary guidance”. The core concepts of adversarial attacks, model robustness, and image generation through diffusion remain central. The work contributes to the ongoing refinement and extension of techniques within the established paradigms, rather than proposing a fundamental shift or revolution. Thus, it is best characterized as an example of normal science, focused on puzzle-solving within the existing framework.