Generative AI is on the rise, enabling everyone to produce realistic content via publicly available interfaces. Especially for guided image generation, diffusion models are changing the creator economy by producing high quality low cost content. In parallel, artists are rising against unruly AI, since their artwork are leveraged, distributed, and dissimulated by large generative models. Our approach, My Art My Choice (MAMC), aims to empower content owners by protecting their copyrighted materials from being utilized by diffusion models in an adversarial fashion. MAMC learns to generate adversarially perturbed "protected" versions of images which can in turn "break" diffusion models. The perturbation amount is decided by the artist to balance distortion vs. protection of the content. MAMC is designed with a simple UNet-based generator, attacking black box diffusion models, combining several losses to create adversarial twins of the original artwork. We experiment on three datasets for various image-to-image tasks, with different user control values. Both protected image and diffusion output results are evaluated in visual, noise, structure, pixel, and generative spaces to validate our claims. We believe that MAMC is a crucial step for preserving ownership information for AI generated content in a flawless, based-on-need, and human-centric way.

翻译：生成式人工智能正在兴起，使每个人都能通过公开接口生成逼真内容。特别是在引导图像生成领域，扩散模型通过生产高质量低成本的内容，正在改变创作者经济。与此同时，艺术家们正联合起来抵制失控的人工智能，因为他们的艺术作品被大型生成模型利用、分发和模仿。我们的方法“我的艺术我选择”（MAMC）旨在赋能内容所有者，保护其受版权保护的材料不被扩散模型以对抗性方式利用。MAMC学习生成经过对抗扰动处理的“受保护”图像版本，从而能“破坏”扩散模型。扰动幅度由艺术家决定，以平衡内容失真与保护程度。MAMC采用基于UNet的简单生成器设计，攻击黑盒扩散模型，通过组合多种损失函数创建原始艺术品的对抗性孪生体。我们在三个数据集上针对不同图像到图像任务进行了实验，使用了不同的用户控制值。从视觉、噪声、结构、像素和生成空间等多个维度评估受保护图像和扩散模型的输出结果，以验证我们的主张。我们相信，MAMC是以无瑕疵、按需且以人为中心的方式保存AI生成内容所有权信息的关键一步。