Recoverable Privacy-Preserving Image Classification through Noise-like
  Adversarial Examples
Jun Liu Jiantao Zhou Jinyu Tian Weiwei Sun
With the increasing prevalence of cloud computing platforms, ensuring data
privacy during the cloud-based image related services such as classification
has become crucial. In this study, we propose a novel privacypreserving image
classification scheme that enables the direct application of classifiers
trained in the plaintext domain to classify encrypted images, without the need
of retraining a dedicated classifier. Moreover, encrypted images can be
decrypted back into their original form with high fidelity (recoverable) using
a secret key. Specifically, our proposed scheme involves utilizing a feature
extractor and an encoder to mask the plaintext image through a newly designed
Noise-like Adversarial Example (NAE). Such an NAE not only introduces a
noise-like visual appearance to the encrypted image but also compels the target
classifier to predict the ciphertext as the same label as the original
plaintext image. At the decoding phase, we adopt a Symmetric Residual Learning
(SRL) framework for restoring the plaintext image with minimal degradation.
Extensive experiments demonstrate that 1) the classification accuracy of the
classifier trained in the plaintext domain remains the same in both the
ciphertext and plaintext domains; 2) the encrypted images can be recovered into
their original form with an average PSNR of up to 51+ dB for the SVHN dataset
and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory
generalization capability on the encryption, decryption and classification
tasks across datasets that are different from the training one; and 4) a
high-level of security is achieved against three potential threat models. The
code is available at https://github.com/csjunjun/RIC.git.