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Re-ID-leak: Membership Inference Attacks Against Person Re-identification
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Re-ID-leak: Membership Inference Attacks Against Person Re-identification
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Journal Article
Re-ID-leak: Membership Inference Attacks Against Person Re-identification
2024
Overview
Person re-identification (Re-ID) has rapidly advanced due to its widespread real-world applications. It poses a significant risk of exposing private data from its training dataset. This paper aims to quantify this risk by conducting a membership inference (MI) attack. Most existing MI attack methods focus on classification models, while Re-ID follows a distinct paradigm for training and inference. Re-ID is a fine-grained recognition task that involves complex feature embedding, and the model outputs commonly used by existing MI algorithms, such as logits and losses, are inaccessible during inference. Since Re-ID models the relative relationship between image pairs rather than individual semantics, we conduct a formal and empirical analysis that demonstrates that the distribution shift of the inter-sample similarity between the training and test sets is a crucial factor for membership inference and exists in most Re-ID datasets and models. Thus, we propose a novel MI attack method based on the distribution of inter-sample similarity, which involves sampling a set of anchor images to represent the similarity distribution that is conditioned on a target image. Next, we consider two attack scenarios based on information that the attacker has. In the “one-to-one” scenario, where the attacker has access to the target Re-ID model and dataset, we propose an anchor selector module to select anchors accurately representing the similarity distribution. Conversely, in the “one-to-any” scenario, which resembles real-world applications where the attacker has no access to the target Re-ID model and dataset, leading to the domain-shift problem, we propose two alignment strategies. Moreover, we introduce the patch-attention module as a replacement for the anchor selector. Experimental evaluations demonstrate the effectiveness of our proposed approaches in Re-ID tasks in both attack scenarios.
