Measure the Psychometric Functions of Deep Learning Models in Encrypted Image Recognition Tasks
Yirui Yao, Pengjing Xu · 2024
The research aims at applying the convolutional neural network (CNN) including LeNet5, AlexNet, and Visual Geometry Group (VGG) with 16 weight layers to directly classify among 4 categories of fully encrypted images that were encrypted by various cryptographic algorithms involving Advanced Encryption Standard (AES), Blowfish, Data Encryption Standard (DES), and Triple DES (TDES) into without decryption to establish a secure image querying technique. The investigation was implemented with three concrete tasks. Firstly, used CNN models to recognize enciphered images with different encryption algorithms or cryptographic keys. Secondly, applied CNN models to classify enciphered images with simulated inference of Gaussian noise. Thirdly, employed CNN models to identify encrypted images with simulated inference of the reduction of contrast ratios.