Identity Inference from CLIP Models using Only Textual Data

• URL: http://arxiv.org/abs/2405.14517v1
• Date: Thu, 23 May 2024 12:54:25 GMT
• Title: Identity Inference from CLIP Models using Only Textual Data
• Authors: Songze Li, Ruoxi Cheng, Xiaojun Jia,
• Abstract summary: Existing methods for identity inference in CLIP models require querying the model with full PII. Traditional membership inference attacks (MIAs) train shadow models to mimic the behaviors of the target model. We propose a textual unimodal detector (TUNI) in CLIP models, a novel method for ID inference that queries the target model with only text data.
• Score: 12.497110441765274
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread usage of large-scale multimodal models like CLIP has heightened concerns about the leakage of personally identifiable information (PII). Existing methods for identity inference in CLIP models, i.e., to detect the presence of a person's PII used for training a CLIP model, require querying the model with full PII, including textual descriptions of the person and corresponding images (e.g., the name and the face photo of the person). However, this may lead to potential privacy breach of the image, as it may have not been seen by the target model yet. Additionally, traditional membership inference attacks (MIAs) train shadow models to mimic the behaviors of the target model, which incurs high computational costs, especially for large CLIP models. To address these challenges, we propose a textual unimodal detector (TUNI) in CLIP models, a novel method for ID inference that 1) queries the target model with only text data; and 2) does not require training shadow models. Firstly, we develop a feature extraction algorithm, guided by the CLIP model, to extract features from a text description. TUNI starts with randomly generating textual gibberish that were clearly not utilized for training, and leverages their feature vectors to train a system of anomaly detectors. During inference, the feature vector of each test text is fed into the anomaly detectors to determine if the person's PII is in the training set (abnormal) or not (normal). Moreover, TUNI can be further strengthened integrating real images associated with the tested individuals, if available at the detector. Extensive experiments of TUNI across various CLIP model architectures and datasets demonstrate its superior performance over baselines, albeit with only text data.

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