Pre-train Encoders

Using SimCLR, BYOL, SimSiam or MoCo v3 to pre-train encoders:

python main_train.py \
    --dataset <Dataset Name> \
    --data_path <PATH/TO/DATASET/DIR> \
    --arch <Encoder Architecture> \
    --method <Self-supervised Algorithm> \
    --part <Use either a random half, the remaining half, or the entire dataset for training.> \
    --ratio 0.5 \
    --num_workers 8 \
    --gpu <GPU ID>

Using DINO to pre-train encoders:

python dino_train.py \
    --dataset <Dataset Name> \
    --data_path <PATH/TO/DATASET/DIR> \
    --arch <Encoder Architecture> \
    --part <Use either a random half, the remaining half, or the entire dataset for training.> \
    --num_workers 8 \
    --gpu <GPU ID>

Dataset Ownership Verification by Our Method

DOV4CL.py: The code of our method. You can run it in the following way:

python DOV4CL.py \
    --D_public <Public Dataset of Defender> \
    --M_shadow_arch <Encoder Architecture of M_shadow> \
    --M_shadow_dataset <the Training Set of M_shadow> \
    --M_shadow_path <PATH/TO/M_shadow/DIR> \
    --M_suspect_arch <Encoder Architecture of M_suspect> \
    --M_suspect_dataset <the Training Set of M_suspect> \
    --M_suspect_path <PATH/TO/M_suspect/DIR> \
    --n_sample_train <k_public> \
    --n_sample_test <k_private> \
    --n_aug <M> \
    --n_aug_local <N> \
    --n_epoch <K> \
    --lamda <a> \
    --gpu <GPU ID>