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Unlearnable datasets lead to a drastic drop in the generalization performanceof models trained on them by introducing elaborate and imperceptibleperturbations into clean training sets. Many existing defenses, e.g., JPEGcompression and adversarial training, effectively counter UDs based onnorm-constrained additive noise. However, a fire-new type of convolution-basedUDs have been proposed and render existing defenses all ineffective, presentinga greater challenge to defenders. To address this, we express theconvolution-based unlearnable sample as the result of multiplying a matrix by aclean sample in a simplified scenario, and formalize the intra-class matrixinconsistency as $\Theta_{imi}$, inter-class matrix consistency as$\Theta_{imc}$ to investigate the working mechanism of the convolution-basedUDs. We conjecture that increasing both of these metrics will mitigate theunlearnability effect. Through validation experiments that commendably supportour hypothesis, we further design a random matrix to boost both $\Theta_{imi}$and $\Theta_{imc}$, achieving a notable degree of defense effect. Hence, bybuilding upon and extending these facts, we first propose a brand-new imageCOrruption that employs randomly multiplicative transformation viaINterpolation operation to successfully defend against convolution-based UDs.Our approach leverages global pixel random interpolations, effectivelysuppressing the impact of multiplicative noise in convolution-based UDs.Additionally, we have also designed two new forms of convolution-based UDs, andfind that our defense is the most effective against them.

Corrupting Convolution-based Unlearnable Datasets with Pixel-based Image Transformations