  Several learnable image encryption schemes have been developed for
privacy-preserving image classification. This paper focuses on the security
block-based image encryption methods that are learnable and JPEG-friendly.
Permuting divided blocks in an image is known to enhance robustness against
ciphertext-only attacks (COAs), but recently jigsaw puzzle solver attacks have
been demonstrated to be able to restore visual information on the encrypted
images. In contrast, it has never been confirmed whether encrypted images
including noise caused by JPEG-compression are robust. Accordingly, the aim of
this paper is to evaluate the security of compressible and learnable encrypted
images against jigsaw puzzle solver attacks. In experiments, the security
evaluation was carried out on the CIFAR-10 and STL-10 datasets under
JPEG-compression.
