Recent text-to-image (T2I) personalization methods have shown great premise in teaching a diffusion model user-specified concepts given a few images for reusing the acquired concepts in a novel context. With massive efforts being dedicated to personalized generation, a promising extension is personalized editing, namely to edit an image using personalized concepts, which can provide more precise guidance signal than traditional textual guidance. To address this, one straightforward solution is to incorporate a personalized diffusion model with a text-driven editing framework. However, such solution often shows unsatisfactory editability on the source image. To address this, we propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods.

Personalized diffusion models have gained popularity for adapting pre-trained text-to-image models to generate images of specific topics with minimal training data. However, these models are vulnerable to minor adversarial perturbations, leading to degraded performance on corrupted datasets. Such vulnerabilities are further exploited to craft protective perturbations on sensitive images like portraits that prevent unauthorized generation. In response, diffusion-based purification methods have been proposed to remove these perturbations and retain generation performance. However, existing works turn to over-purifying the images, which causes information loss. In this paper, we take a closer look at the fine-tuning process of personalized diffusion models through the lens of shortcut learning. And we propose a hypothesis explaining the manipulation mechanisms of existing perturbation methods, demonstrating that perturbed images significantly deviate from their original prompts in the CLIP-based latent space. This misalignment during fine-tuning causes models to associate noisy patterns with identifiers, resulting in performance degradation. Based on these insights, we introduce a systematic approach to maintain training performance through purification. Our method first purifies the images to realign them with their original semantic meanings in latent space. Then, we introduce contrastive learning with negative tokens to decouple the learning of clean identities from noisy patterns, which shows a strong potential capacity against adaptive perturbation. Our study uncovers shortcut learning vulnerabilities in personalized diffusion models and provides a firm evaluation framework for future protective perturbation research. Code is available at https://github.com/liuyixin-louis/DiffShortcut.