Generative AI (GenAI) is transforming creative workflows through the
capability to synthesize and manipulate images via high-level prompts. Yet
creatives are not well supported to receive recognition or reward for the use
of their content in GenAI training. To this end, we propose ProMark, a causal
attribution technique to attribute a synthetically generated image to its
training data concepts like objects, motifs, templates, artists, or styles. The
concept information is proactively embedded into the input training images
using imperceptible watermarks, and the diffusion models (unconditional or
conditional) are trained to retain the corresponding watermarks in generated
images. We show that we can embed as many as $2^{16}$ unique watermarks into
the training data, and each training image can contain more than one watermark.
ProMark can maintain image quality whilst outperforming correlation-based
attribution. Finally, several qualitative examples are presented, providing the
confidence that the presence of the watermark conveys a causative relationship
between training data and synthetic images.

通过生成式 AI (GenAI) 来合成和操纵图像的高级提示，然而创作者在 GenAI 训练中无法获得他们内容使用的认可或回报。因此，我们提出了一种叫做 ProMark 的因果归因技术，将合成生成的图像归因于其训练数据的概念，如对象、主题、模板、艺术家或风格。我们展示了我们可以将多达 $2^{16}$ 个独特的水印嵌入到训练数据中，并且每个训练图像可以包含多个水印。ProMark 在保持图像质量的同时，表现优于基于相关性的归因方法。最后，我们提供了几个定性示例，证明水印的存在传达了训练数据和合成图像之间的因果关系。

ProMark: 主动扩散数字水印用于因果归因

ProMark: Proactive Diffusion Watermarking for Causal Attribution

Deep neural networks have demonstrated impressive accuracy in supervised
learning tasks. However, their lack of transparency makes it hard for humans to
trust their results, especially in safe-critic domains such as healthcare. To
address this issue, recent explanation-guided learning approaches proposed to
align the gradient-based attention map to image regions annotated by human
experts, thereby obtaining an intrinsically human-centered model. However, the
attention map these methods are based on may fail to causally attribute the
model predictions, thus compromising their validity for alignment. To address
this issue, we propose a novel human-centered framework based on counterfactual
generation. In particular, we utilize the counterfactual generation's ability
for causal attribution to introduce a novel loss called the CounterFactual
Alignment (CF-Align) loss. This loss guarantees that the features attributed by
the counterfactual generation for the classifier align with the human
annotations. To optimize the proposed loss that entails a counterfactual
generation with an implicit function form, we leverage the implicit function
theorem for backpropagation. Our method is architecture-agnostic and, therefore
can be applied to any neural network. We demonstrate the effectiveness of our
method on a lung cancer diagnosis dataset, showcasing faithful alignment to
humans.

基于反事实生成的人类中心框架实现对深度神经网络的解释引导学习，并显示对人体的准确对齐，从而在肺癌诊断数据集上展示出的有效性。