Reconstructing Training Data From Real-World Models Trained with Transfer Learning

ICLR 2025 Conference Submission1560 Authors

Keywords: data reconstruction, memorization, privacy

TL;DR: We show reconstruction of training samples from models trained on embeddings of large-scale pretrained models via transfer learning

Abstract: Current methods for reconstructing the training data from trained classifiers are restricted to very small models, limited training set sizes, and low-resolution images. Such restrictions hinder their applicability to real-world scenarios. In this paper, we present a novel approach enabling data reconstruction in realistic settings for models trained on high-resolution images. Our method adapts the reconstruction scheme of Haim et al. [2022] to real-world scenarios -- specifically, targeting models trained via transfer learning over image embeddings of large pre-trained models like DINO-ViT and CLIP. Our work employs data reconstruction in the embedding space rather than in the image space, showcasing its applicability beyond visual data. Moreover, we introduce a novel clustering-based method to identify good reconstructions from thousands of candidates. This significantly improves on previous works that relied on knowledge of the training set to identify good reconstructed images. Our findings shed light on a potential privacy risk for data leakage from models trained using transfer learning methods.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 1560