In recent years, Deep Neural Networks (DNN) have emerged as a practical method for image recognition. The raw data, which contain sensitive information, are generally exploited within the training process. However, when the training process is outsourced to a third-party organization, the raw data should be desensitized before being transferred to protect sensitive information. Although masks are widely applied to hide important sensitive information, preventing inpainting masked images is critical, which may restore the sensitive information. The corresponding models should be adjusted for the masked images to reduce the degradation of the performance for recognition or classification tasks due to the desensitization of images. In this paper, we propose a mask-based image desensitization approach while supporting recognition. This approach consists of a mask generation algorithm and a model adjustment method. We propose exploiting an interpretation algorithm to maintain critical information for the recognition task in the mask generation algorithm. In addition, we propose a feature selection masknet as the model adjustment method to improve the performance based on the masked images. Extensive experimentation results based on multiple image datasets reveal significant advantages (up to 9.34% in terms of accuracy) of our approach for image desensitization while supporting recognition.

翻译：近年来，深度神经网络已成为图像识别的实用方法。包含敏感信息的原始数据通常被用于训练过程。然而，当训练过程外包给第三方机构时，原始数据在传输前需进行脱敏处理以保护敏感信息。尽管掩码被广泛用于隐藏重要的敏感信息，但防止对被掩码图像进行修复至关重要，因为修复可能恢复敏感信息。为减少图像脱敏对识别或分类任务性能的降低，需对相应模型进行针对掩码图像的调整。本文提出一种支持识别功能的基于掩码的图像脱敏方法。该方法包含掩码生成算法与模型调整方法。在掩码生成算法中，我们利用解释算法保留识别任务的关键信息；在模型调整方法中，提出特征选择掩码网络以提升基于掩码图像的识别性能。基于多个图像数据集的大量实验结果表明，本方法在支持识别的同时，在图像脱敏性能上具有显著优势（准确率提升最高达9.34%）。