In forensic investigations of machine learning models, techniques that determine a model's data domain play an essential role, with prior work relying on large-scale corpora like ImageNet to approximate the target model's domain. Although such methods are effective in finding broad domains, they often struggle in identifying finer-grained classes within those domains. In this paper, we introduce an enhanced approach to determine not just the general data domain (e.g., human face) but also its specific attributes (e.g., wearing glasses). Our approach uses an image embedding model as the encoder and a generative model as the decoder. Beginning with a coarse-grained description, the decoder generates a set of images, which are then presented to the unknown target model. Successful classifications by the model guide the encoder to refine the description, which in turn, are used to produce a more specific set of images in the subsequent iteration. This iterative refinement narrows down the exact class of interest. A key strength of our approach lies in leveraging the expansive dataset, LAION-5B, on which the generative model Stable Diffusion is trained. This enlarges our search space beyond traditional corpora, such as ImageNet. Empirical results showcase our method's performance in identifying specific attributes of a model's input domain, paving the way for more detailed forensic analyses of deep learning models.
BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）
ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge
National University of Singapore（新加坡国立大学）；Washington State University（华盛顿州立大学）；Hanoi University of Science and Technology（河内科学技术大学）；National Institute of Advanced Industrial Science and Technology（国家先进工业科学与技术研究院）
Black-Box Auditing of Quantum Model: Lifted Differential Privacy with Quantum Canaries
Faculty of Engineering and IT, University of Technology Sydney（悉尼大学工程与信息学院）；School of IT, Deakin University（德金大学信息学院）；Center for AI Research (CAIR), University of Agder (UiA)（阿格德大学人工智能研究中心）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）
Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Transformers
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Shanghai Jiao Tong University（上海交通大学）；Sichuan University（四川大学）；Big Data and AI Lab, ICBC（中国工商银行大数据与人工智能实验室）；Hong Kong University of Science and Technology, Guangzhou（香港科技大学广州校区）
