生成式人工智能正在兴起，使每个人都可以通过公开可用的界面生成真实的内容。特别是对于引导图像生成，扩散模型正在通过生产高质量低成本内容来改变创作者经济。与此同时，艺术家们正在反抗不守规矩的人工智能，因为他们的艺术作品被大型生成模型利用、分发和掩盖。我们的方法“我的艺术我的选择”(MAMC) 旨在通过保护内容所有者的受版权保护的材料免遭传播模型以对抗性的方式利用来赋予内容所有者权力。MAMC 学习生成图像的对抗性扰动“受保护”版本，从而可以“破坏”扩散模型。扰动量由艺术家决定，以平衡失真与内容保护。MAMC 设计有一个简单的基于 UNet 的生成器，攻击黑盒扩散模型，结合多种损失来创建原始艺术品的对抗双胞胎。我们使用不同的用户控制值对用于各种图像到图像任务的三个数据集进行实验。受保护的图像和扩散输出结果都在视觉、噪声、结构、像素和生成空间中进行评估，以验证我们的主张。我们相信，MAMC 是以完美、基于需求和以人为中心的方式保存人工智能生成内容的所有权信息的关键一步。受保护的图像和扩散输出结果都在视觉、噪声、结构、像素和生成空间中进行评估，以验证我们的主张。我们相信，MAMC 是以完美、基于需求和以人为中心的方式保存人工智能生成内容的所有权信息的关键一步。受保护的图像和扩散输出结果都在视觉、噪声、结构、像素和生成空间中进行评估，以验证我们的主张。我们相信，MAMC 是以完美、基于需求和以人为中心的方式保存人工智能生成内容的所有权信息的关键一步。 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.