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Privacy preserving machine learning is an active area of research usuallyrelying on techniques such as homomorphic encryption or secure multipartycomputation. Recent novel encryption techniques for performing machine learningusing deep neural nets on images have recently been proposed by Tanaka andSirichotedumrong, Kinoshita, and Kiya. We present new chosen-plaintext andciphertext-only attacks against both of these proposed image encryption schemesand demonstrate the attacks' effectiveness on several examples.

Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks