Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image's source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL-E 3 API verifies the real-world applicability of our solution.
A Simple and Efficient Baseline for Data Attribution on Images
University of Maryland（马里兰大学）；New York University（纽约大学）；ELLIS Institute Tübingen（蒂宾根ELLIS研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）
Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peking University（北京大学）
Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)
School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学系）；Department of Computer Science, University of Warwick（沃里克大学计算机科学系）
OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution
Adobe Research（Adobe研究院）；University of Surrey（萨里大学）
图像讲述了强大的故事，但并不总是可信的。将图像匹配回可信来源（归因）使用户能够对他们在网上遇到的图像做出更明智的判断。我们提出了一个强大的图像哈希算法来执行这样的匹配。我们的哈希对微妙的、突出的视觉细节的操纵很敏感，这些细节可以大大改变图像所讲述的故事。然而，散列对于良性转换（质量，编解码器，大小，形状等的变化）是不变的。在网上重新发布的图片所经历的。我们的主要贡献是OSCAR-Net（Object-centric Scene Graph Attention for Image Attribution Network）;这是一个强大的图像哈希模型，灵感来自于最近Transformers在视觉领域的成功。OSCAR-Net构建了一个场景图表示，它关注每个对象的视觉外观及其空间关系的细粒度变化。该网络通过原始图像和操作图像的数据集上的对比学习进行训练，从而产生用于内容指纹识别的最新图像散列，该图像散列可扩展到数百万个图像。
