  Privacy preserving machine learning is an active area of research usually
relying on techniques such as homomorphic encryption or secure multiparty
computation. Recent novel encryption techniques for performing machine learning
using deep neural nets on images have recently been proposed by Tanaka and
Sirichotedumrong, Kinoshita, and Kiya. We present new chosen-plaintext and
ciphertext-only attacks against both of these proposed image encryption schemes
and demonstrate the attacks' effectiveness on several examples.
