Probabilistic Extensions: A One-Step Framework for Finding Rectangle Attacks and Beyond
Ling Song, Qianqian Yang, Yincen Chen, Lei Hu, Jian Weng
Abstract
In differential-like attacks, the process typically involves extending a distinguisher forward and backward with probability 1 for some rounds and recovering the key involved in the extended part. Particularly in rectangle attacks, a holistic key recovery strategy can be employed to yield the most efficient attacks tailored to a given distinguisher. In this paper, we treat the distinguisher and the extended part as an integrated entity and give a one-step framework for finding rectangle attacks with the purpose of reducing the overall complexity or attacking more rounds. In this framework, we propose to allow probabilistic differential propagations in the extended part and incorporate the holistic recovery strategy. Additionally, we introduce the ``split-and-bunch technique'' to further reduce the time complexity. Beyond rectangle attacks, we extend these foundational concepts to encompass differential attacks as well. To demonstrate the efficiency of our framework, we apply it to Deoxys-BC-384, SKINNY, ForkSkinny, and CRAFT, achieving a series of refined and improved rectangle attacks and differential attacks. Notably, we obtain the first 15-round attack on Deoxys-BC-384, narrowing its security margin to only one round. Furthermore, our differential attack on CRAFT extends to 23 rounds, covering two more rounds than the previous best attacks.
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