  The success of deep learning is partly attributed to the availability of
massive data downloaded freely from the Internet. However, it also means that
users' private data may be collected by commercial organizations without
consent and used to train their models. Therefore, it's important and necessary
to develop a method or tool to prevent unauthorized data exploitation. In this
paper, we propose ConfounderGAN, a generative adversarial network (GAN) that
can make personal image data unlearnable to protect the data privacy of its
owners. Specifically, the noise produced by the generator for each image has
the confounder property. It can build spurious correlations between images and
labels, so that the model cannot learn the correct mapping from images to
labels in this noise-added dataset. Meanwhile, the discriminator is used to
ensure that the generated noise is small and imperceptible, thereby remaining
the normal utility of the encrypted image for humans. The experiments are
conducted in six image classification datasets, consisting of three natural
object datasets and three medical datasets. The results demonstrate that our
method not only outperforms state-of-the-art methods in standard settings, but
can also be applied to fast encryption scenarios. Moreover, we show a series of
transferability and stability experiments to further illustrate the
effectiveness and superiority of our method.
