Instance-Level Data-Use Auditing of Visual ML Models
This addresses accountability for data owners in visual ML, though it is incremental as it builds on prior work.
The paper tackles the problem of unauthorized data use in ML by developing the first proactive, instance-level auditing method, which reveals that two state-of-the-art approximate unlearning methods fail to remove data influence even with a 10% utility sacrifice.
The growing trend of legal disputes over the unauthorized use of data in machine learning (ML) systems highlights the urgent need for reliable data-use auditing mechanisms to ensure accountability and transparency in ML. We present the first proactive, instance-level, data-use auditing method designed to enable data owners to audit the use of their individual data instances in ML models, providing more fine-grained auditing results than previous work. To do so, our research generalizes previous work integrating black-box membership inference and sequential hypothesis testing, expanding its scope of application while preserving the quantifiable and tunable false-detection rate that is its hallmark. We evaluate our method on three types of visual ML models: image classifiers, visual encoders, and vision-language models (Contrastive Language-Image Pretraining (CLIP) and Bootstrapping Language-Image Pretraining (BLIP) models). In addition, we apply our method to evaluate the performance of two state-of-the-art approximate unlearning methods. As a noteworthy second contribution, our work reveals that neither method successfully removes the influence of the unlearned data instances from image classifiers and CLIP models, even if sacrificing model utility by $10\%$.