Anti-DreamBooth: Protecting users from personalized text-to-image synthesis (2303.15433v2)

Abstract: Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at https://github.com/VinAIResearch/Anti-DreamBooth.git.

Insightful Overview of Anti-DreamBooth: Protecting Users from Personalized Text-to-Image Synthesis

The paper "Anti-DreamBooth: Protecting users from personalized text-to-image synthesis" addresses the societal challenges posed by text-to-image diffusion models, especially when leveraged for personalization through methods like DreamBooth. While these models offer remarkable capabilities in generating images that align with given textual prompts, their misuse raises significant ethical and privacy concerns. The authors present a novel solution termed Anti-DreamBooth, which aims to proactively defend individuals against unauthorized exploitation of their visual identity.

Technical Contributions and Results

The authors introduce a system based on the principle of adversarial attacks, where they perturb user images with subtle noise alterations prior to their public release. These perturbations are crafted such that any personalized model, like DreamBooth, trained on the perturbed images fails to generate coherent outputs. This framework serves as an image cloaking strategy specifically targeting diffusion models.

The paper explores multiple algorithms for generating these adversarial perturbations:

1. Fully-trained Surrogate Model Guidance (FSMG): It uses a surrogate model trained on clean data to guide the noise generation process. This approach is grounded in the typical adversarial strategy of utilizing a fixed surrogate model to craft perturbations, aiming to mislead any subsequent personalization attempts.
2. Alternating Surrogate and Perturbation Learning (ASPL): This method iteratively optimizes the surrogate model and the perturbations. It alternates between finetuning the surrogate on clean images and updating the perturbations, making it more robust and adaptive to changes in the adversary's strategy.

The performance of these methods is rigorously evaluated on VGGFace2 and CelebA-HQ datasets, demonstrating the robustness of Anti-DreamBooth under convenient, adverse, and uncontrolled settings. The defense's effectiveness is consistently proven with diverse metrics like Face Detection Failure Rate (FDFR), Identity Score Matching (ISM), SER-FQA, and BRISQUE, despite differences in model, prompt, and term usage between training and testing phases.

Implications and Future Directions

The implications of Anti-DreamBooth are multifaceted, extending beyond academic inquiry to practical applications in user privacy, digital rights management, and ethical AI deployment. By proactively disrupting unauthorized personalized image synthesis, this research contributes to the broader discourse on AI regulation and digital privacy protections.

Looking forward, potential advancements in this space could address limitations in perturbation visibility and robustness enhancement against more sophisticated adversarial removal techniques. The development of universal or context-specific perturbations adaptable to broader image types and applications could also expand the scope of Anti-DreamBooth's applicability.

Additionally, the paper's framework provides a solid foundation for exploring adaptive defenses in other content generation domains like video synthesis or voice modification, thereby setting the stage for comprehensive solutions in AI-driven content manipulation.

In conclusion, the Anti-DreamBooth framework distinctly positions itself as a relevant and timely contribution to safeguarding privacy in the era of personalized AI models. The synergistic combination of adversarial strategies and image cloaking provides an effective defense mechanism worthy of further refinement and deployment.