Researchers proposed Inverse Knowledge Distillation (IKD), a mechanism to enhance adversarial transferability by maximizing the prediction-distribution discrepancy between benign and adversarial samples. This method uses a soft-label objective to enrich attacks with surrogate directions, and its analysis derives a lower bound on Fisher-subspace overlap between surrogate and target models.