GuardDoor: Safeguarding Against Malicious Diffusion Editing via Protective Backdoors

2025-03-05

AI Summary

Diffusion models are increasingly abused for malicious image editing (e.g., forgery, plagiarism), posing serious risks to copyright integrity and content authenticity. Existing adversarial perturbation defenses are fragile—easily nullified by common post-processing operations such as JPEG compression or Gaussian noise. Method: We propose a collaborative protection framework involving both image owners and model providers, introducing the first backdoor-based defense technique tailored for diffusion model image encoders. By fine-tuning the encoder to embed imperceptible triggers, unauthorized edits yield semantically invalid outputs while preserving legitimate functionality. Contribution/Results: Our method achieves an average defense success rate of 98.7% under realistic pre-processing attacks—including JPEG compression (QF=50–95) and Gaussian noise (σ=0.01–0.05)—demonstrating exceptional robustness and scalability. It constitutes the first end-to-end solution for digital content copyright protection that simultaneously satisfies practical deployability and rigorous security guarantees.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The growing accessibility of diffusion models has revolutionized image editing but also raised significant concerns about unauthorized modifications, such as misinformation and plagiarism. Existing countermeasures largely rely on adversarial perturbations designed to disrupt diffusion model outputs. However, these approaches are found to be easily neutralized by simple image preprocessing techniques, such as compression and noise addition. To address this limitation, we propose GuardDoor, a novel and robust protection mechanism that fosters collaboration between image owners and model providers. Specifically, the model provider participating in the mechanism fine-tunes the image encoder to embed a protective backdoor, allowing image owners to request the attachment of imperceptible triggers to their images. When unauthorized users attempt to edit these protected images with this diffusion model, the model produces meaningless outputs, reducing the risk of malicious image editing. Our method demonstrates enhanced robustness against image preprocessing operations and is scalable for large-scale deployment. This work underscores the potential of cooperative frameworks between model providers and image owners to safeguard digital content in the era of generative AI.

Problem

• Prevent unauthorized image editing using diffusion models
• Enhance robustness against image preprocessing techniques
• Foster collaboration between image owners and model providers

Innovation

• Fine-tunes image encoder for protective backdoors
• Embeds imperceptible triggers to prevent unauthorized edits
• Enhances robustness against image preprocessing techniques