Interpreting Structured Perturbations in Image Protection Methods for Diffusion Models

Recent image protection mechanisms such as Glaze and Nightshade introduce imperceptible, adversarially designed perturbations intended to disrupt downstream text-to-image generative models. While their empirical effectiveness has been demonstrated, the internal structure, detectability, and representational behavior of these perturbations remain poorly understood. In this study, we demonstrated a systematic explainable AI analysis of image protection perturbations using a unified framework that integrates white-box feature-space inspection and black-box signal-level probing. Through latent-space clustering, feature-channel activation analysis, occlusion-based spatial sensitivity mapping, and frequency-domain spectral characterization, we revealed that modern protection mechanisms operate as structured, low-entropy perturbations that remain tightly coupled to underlying image content across representational, spatial, and spectral domains in all evaluated cases. We showed that protected images preserve content-driven feature organization with protection-specific substructure rather than inducing global representational drift. Detectability is governed by interacting effects of perturbation entropy, spatial deployment, and frequency alignment as revealed through combined synthetic and spectral analyses, with sequential protection amplifying detectable structure rather than suppressing it. Frequency-domain analysis further demonstrated that Glaze and Nightshade redistribute energy along dominant image-aligned frequency axes rather than introducing spectrally diffuse noise. These results suggested that contemporary image protection operates through structured feature-level deformation rather than semantic dislocation, providing mechanistic insight into why protection signals remain visually subtle yet consistently detectable. This work advances the interpretability of adversarial image protection and informs the design of future defenses and detection strategies for generative AI systems.