  Owing much to the revolution of information technology, the recent progress
of deep learning benefits incredibly from the vastly enhanced access to data
available in various digital formats. However, in certain scenarios, people may
not want their data being used for training commercial models and thus studied
how to attack the learnability of deep learning models. Previous works on
learnability attack only consider the goal of preventing unauthorized
exploitation on the specific dataset but not the process of restoring the
learnability for authorized cases. To tackle this issue, this paper introduces
and investigates a new concept called "learnability lock" for controlling the
model's learnability on a specific dataset with a special key. In particular,
we propose adversarial invertible transformation, that can be viewed as a
mapping from image to image, to slightly modify data samples so that they
become "unlearnable" by machine learning models with negligible loss of visual
features. Meanwhile, one can unlock the learnability of the dataset and train
models normally using the corresponding key. The proposed learnability lock
leverages class-wise perturbation that applies a universal transformation
function on data samples of the same label. This ensures that the learnability
can be easily restored with a simple inverse transformation while remaining
difficult to be detected or reverse-engineered. We empirically demonstrate the
success and practicability of our method on visual classification tasks.
