Abstract
In the domain of black-box model extraction, conventional methods reliant on soft labels or surrogate datasets struggle with scaling to high-dimensional input spaces and managing the complexity of an extensive array of interrelated classes. In this work, we present a novel approach that utilizes SHAP (SHapley Additive exPlanations) to enhance synthetic data generation. SHAP quantifies the individual contributions of each input feature towards the victim model's output, facilitating the optimization of an energy-based GAN towards a desirable output. This method significantly boosts performance, achieving a 16.45% increase in the accuracy of image classification models and extending to video classification models with an average improvement of 26.11% and a maximum of 33.36% on challenging datasets such as UCF11, UCF101, Kinetics 400, Kinetics 600, and Something-Something V2. We further demonstrate the effectiveness and practical utility of our method under various scenarios, including the availability of top-k prediction probabilities, top-k prediction labels, and top-1 labels.
Abstract (translated)
在领域黑盒模型提取中，传统方法在软标签或代理数据集上依赖，很难扩展到高维输入空间并管理广泛的相互类别的复杂性。在本文中，我们提出了一个新方法，利用SHAP（SHapley Additive exPlanations）增强合成数据生成。SHAP计算每个输入特征对受害者模型输出的单独贡献，从而促进基于能量的GAN优化到一个理想的输出。这种方法显著提高了性能，将图像分类模型的准确率提高了16.45%，并将视频分类模型的平均改进扩展至具有挑战性的数据集（如UCF11，UCF101，Kinetics 400，Kinetics 600和Something-Something V2），这些数据集的准确率提高至33.36%。我们进一步展示了我们的方法在不同场景下的有效性和实际应用价值，包括存在Top-k预测概率、Top-k预测标签和Top-1标签的情况。
URL
https://arxiv.org/abs/2408.02140
