Fault-propagation Pattern Based DFA on SPN Structure Block Ciphers using Bitwise Permutation, with Application to PRESENT and PRINTcipher

This paper proposes a novel fault-propagation pattern based differential fault analysis method - FPP-DFA, and proves its feasibility on SPN structure block ciphers using bitwise permutation, such as PRESENT and PRINTcipher. Simulated experiments demonstrate that, with the fault model of injecting one nibble fault into the r-2th round substitution layer, on average 8 and 16 faulty samples can reduce the master key search space of PRESENT-80/128 to $2^{14.7}$ and $2^{21.1}$ respectively, and 12 and 24 effective faulty samples can reduce the master key search space of PRINTcipher-48/96 to $2^{13.7}$ and $2^{22.8}$ respectively; with the fault model of injecting one nibble fault into the r-3th round substitution layer, 8 samples can reduce the master key search space of PRINTCipher-96 to $2^{18.7}$

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. Code is available at https://github.com/uyzhang/JSeg (Jittor) and https://github.com/Visual-Attention-Network/SegNeXt (Pytorch).Comment: SegNeXt, a simple CNN for semantic segmentation. Code is availabl