我的艺术我做主：对抗无约束AI的对抗性保护
My Art My Choice: Adversarial Protection Against Unruly AI
AI总结：
针对扩散模型滥用艺术家作品的问题，提出MAMC方法，通过UNet生成对抗性扰动图像破坏扩散模型，由艺术家控制扰动程度，实验验证其有效保护版权内容。
摘要：
生成式人工智能正在崛起，使得每个人都能通过公开可用的接口生成逼真的内容。特别是在引导图像生成方面，扩散模型通过生成高质量、低成本的内容正在改变创作者经济。与此同时，艺术家们正在反抗无约束的AI，因为他们的艺术作品被大型生成模型利用、分发和仿冒。我们的方法“我的艺术我做主”（MAMC）旨在通过以对抗性方式保护内容所有者的版权材料，使其不被扩散模型利用。MAMC学习生成对抗性扰动的“受保护”图像版本，这些版本能够“破坏”扩散模型。扰动程度由艺术家决定，以平衡内容失真与保护之间的权衡。MAMC采用基于简单UNet的生成器设计，攻击黑盒扩散模型，结合多种损失函数来生成原始艺术品的对抗性孪生版本。我们在三个数据集上针对各种图像到图像任务进行了实验，使用了不同的用户控制值。我们分别从视觉、噪声、结构、像素和生成空间对受保护图像和扩散模型输出结果进行评估，以验证我们的主张。我们相信，MAMC是朝着以无瑕疵、按需和以人为本的方式保护AI生成内容所有权信息迈出的关键一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
