Adapter Shield: A Unified Framework with Built-in Authentication for Preventing Unauthorized Zero-Shot Image-to-Image Generation

2025-11-24

AI Summary

To address intellectual property risks arising from unauthorized identity cloning and artistic style mimicry in zero-shot image-to-image generation, this paper proposes the first unified defense framework tailored for diffusion models. Our method integrates reversible image encryption with multi-objective adversarial perturbations, injecting key-dependent encrypted representations into the image encoder’s embedding space. Authorized users recover high-fidelity content via a key-driven decryption module, whereas unauthorized generation yields severely distorted outputs. This work pioneers fine-grained access control jointly optimized with end-to-end authentication, enabling both high-fidelity authorized synthesis and robust suppression of illicit usage. Experiments demonstrate significant improvements over state-of-the-art methods on Face ID protection and style-transfer defense tasks, achieving an optimal balance among security, usability, and compatibility with existing diffusion-based pipelines.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Authentication, authorization, and access control
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

With the rapid progress in diffusion models, image synthesis has advanced to the stage of zero-shot image-to-image generation, where high-fidelity replication of facial identities or artistic styles can be achieved using just one portrait or artwork, without modifying any model weights. Although these techniques significantly enhance creative possibilities, they also pose substantial risks related to intellectual property violations, including unauthorized identity cloning and stylistic imitation. To counter such threats, this work presents Adapter Shield, the first universal and authentication-integrated solution aimed at defending personal images from misuse in zero-shot generation scenarios. We first investigate how current zero-shot methods employ image encoders to extract embeddings from input images, which are subsequently fed into the UNet of diffusion models through cross-attention layers. Inspired by this mechanism, we construct a reversible encryption system that maps original embeddings into distinct encrypted representations according to different secret keys. The authorized users can restore the authentic embeddings via a decryption module and the correct key, enabling normal usage for authorized generation tasks. For protection purposes, we design a multi-target adversarial perturbation method that actively shifts the original embeddings toward designated encrypted patterns. Consequently, protected images are embedded with a defensive layer that ensures unauthorized users can only produce distorted or encrypted outputs. Extensive evaluations demonstrate that our method surpasses existing state-of-the-art defenses in blocking unauthorized zero-shot image synthesis, while supporting flexible and secure access control for verified users.

Problem

• Prevents unauthorized zero-shot image-to-image generation
• Protects personal images from identity cloning and style imitation
• Provides authentication-based defense with encryption and adversarial perturbations

Innovation

• Reversible encryption system for image embeddings
• Multi-target adversarial perturbation for active protection
• Authentication-integrated framework for secure access control