Adversarial Attack via Latent Feature Attribution of Diffusion Models

Traditional adversarial attacks are constrained by \({l_p}\) norm limits, resulting in adversarial examples with high-frequency noise that compromise their imperceptibility. In contrast, attacks using diffusion models without \({l_p}\) norm constraints can generate visually more covert adversarial examples; however, existing methods generally suffer from overfitting of the surrogate model and rely on equal-step-size perturbation updates that treat all spatial features equally, leading to poor transferability. To address these issues, we propose an adversarial attack method based on latent feature attribution using diffusion models (LFA-Diff). First, we apply random masking and inject Gaussian noise into the latent space. By aggregating gradients after multiple transformations, we generate a normalized weight map and perform feature attribution based on it, focusing perturbations on important features that are common across models, thereby improving the transferability of adversarial examples; Second, we generate perturbations constrained by the reconstruction error of a latent diffusion model VAE and use a momentum-based iterative algorithm to adaptively update the perturbations, ensuring that the latent variables retain visual naturalness after being restored to the pixel space. Experimental results demonstrate that the proposed method achieves significantly higher cross-model average attack success rates while maintaining superior image imperceptibility. Source code: https://github.com/shichiale/LFA-Diff .