On Mask-based Image Set Desensitization with Recognition Support
Qilong Li Ji Liu Yifan Sun Chongsheng Zhang Dejing Dou
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.