# QCLR

Mainstream contrastive models for self-supervised skeleton-based action recognition had almost no defense against adversarial attacks, where tiny perturbations to skeleton data could sharply degrade recognition. QCLR adds quantization as a plug-in module at two levels of the encoding pipeline, mapping continuous inputs and features to discrete codebooks so small perturbations are absorbed and gradient propagation is broken; integrated into SkeletonCLR, AimCLR and ActCLR on NTU-60, NTU-120 and PKU-MMD, it cuts PGD and SMART attack success rates to single digits while keeping classification accuracy comparable to the original models.

Mainstream contrastive models for self-supervised skeleton-based action recognition had almost no defense against adversarial attacks, where tiny perturbations to skeleton data could sharply degrade recognition. QCLR adds quantization as a plug-in module at two levels of the encoding pipeline, mapping continuous inputs and features to discrete codebooks so small perturbations are absorbed and gradient propagation is broken; integrated into SkeletonCLR, AimCLR and ActCLR on NTU-60, NTU-120 and PKU-MMD, it cuts PGD and SMART attack success rates to single digits while keeping classification accuracy comparable to the original models.