Robotics presents a promising opportunity for enhancing bathing assistance, potentially to alleviate labor shortages and reduce care costs, while offering consistent and gentle care for individuals with physical disabilities. However, ensuring flexible and efficient cleaning of the human body poses challenges as it involves direct physical contact between the human and the robot, and necessitates simple, safe, and effective control. In this paper, we introduce a soft, expandable robotic manipulator with embedded capacitive proximity sensing arrays, designed for safe and efficient bed bathing assistance. We conduct a thorough evaluation of our soft manipulator, comparing it with a baseline rigid end effector in a human study involving 12 participants across 96 bathing trails. Our soft manipulator achieves an an average cleaning effectiveness of 88.8% on arms and 81.4% on legs, far exceeding the performance of the baseline. Participant feedback further validates the manipulator’s ability to maintain safety, comfort, and thorough cleaning. https://sites.google.com/view/softbathing.

The key collision attack was proposed as an open problem in key-committing security in Authenticated Encryption (AE) schemes like AES-GCM and ChaCha20Poly1305. In ASIACRYPT 2024, Taiyama et al. introduce a novel type of key collision—target-plaintext key collision (TPKC) for AES. Depending on whether the plaintext is fixed, TPKC can be divided into fixed-TPKC and free-TPKC, which can be directly converted into collision attacks and semi-free-start collision attacks on the Davies-Meyer (DM) hashing mode. In this paper, we propose a new rebound attack framework leveraging a time-memory tradeoff strategy, enabling practical key collision attacks with optimized complexity. We also present an improved automatic method for findingrebound-friendlydifferential characteristics by controlling the probabilities in the inbound and outbound phases, allowing the identified characteristics to be directly used inrebound-basedkey collision attacks. Our analysis reveals that the 2-round AES-128 fixed-TPKC attack proposed by Taiyama et al. is, in fact, a free-TPKC attack. This distinction is significant, as fixed-TPKC attacks are substantially more difficult than their free-TPKC counterparts. By integrating our improved automatic method with a new rebound attack framework, we successfully identify a new differential characteristic for the 2-round AES-128 fixed-TPKC attack and develope the first practical fixed-TPKC attack against 2-round AES-128. Additionally, we present practical fixed-TPKC attacks against 5-round AES-192 and 3-round Kiasu-BC, along with a practical free-TPKC attack against 6-round Kiasu-BC. Furthermore, we reduce time complexities for free-TPKC and fixed-TPKC attacks on other AES variants.

Although existing super-resolution (SR) techniques have made great progress, they are often tailored for either paired or unpaired scenery, thus may result in poor migration ability. In this work, we propose a generalized Frequency Domain Separation Network (FDSNet) for both paired and unpaired SR settings. Firstly, through statistical analysis, we found that real-world low-resolution (LR) images and high-resolution (HR) images differ greatly in high frequencies but less in low frequencies. Inspired by this, we perform high and low-frequency separation of LR images and guide our model to reconstruct the HR contents in the different frequency domains. Then, according to the varying attention on frequencies of traditional CNN and Transformer models, we design a parallel pipeline: LFNet based on Transformer for low-frequency feature extraction, and HFNet based on CNN for high frequencies. In LFNet, to further alleviate the high complexity and data dependency of Transformer, Simplified Multi-head Self Attention (SMSA) is proposed at a low computational cost. And original MLP is replaced by our Spatial Enhancement MLP (SEMLP) to take full advantage of local spatial contexts. Finally, to further facilitate frequency separation and learning, a Frequency attention block is designed to impose guidance on high frequencies. Experiments indicate that our FDSNet achieves promising performance in terms of quantitative and qualitative evaluations while enjoying a faster speed and much fewer parameters.

Grasping has long been considered an important and practical task in robotic manipulation. Yet achieving robust and efficient grasps of diverse objects is challenging, since it involves gripper design, perception, control, and learning, etc. Recent learning-based approaches have shown excellent performance in grasping a variety of novel objects. However, these methods either are typically limited to one single grasping mode or else more end effectors are needed to grasp various objects. In addition, gripper design and learning methods are commonly developed separately, which may not adequately explore the ability of a multimodal gripper. In this article, we present a deep reinforcement learning (DRL) framework to achieve multistage hybrid robotic grasping with a new soft multimodal gripper. A soft gripper with three grasping modes (i.e.,enveloping,sucking, andenveloping_then_sucking) can both deal with objects of different shapes and grasp more than one object simultaneously. We propose a novel hybrid grasping method integrated with the multimodal gripper to optimize the number of grasping actions. We evaluate the DRL framework under different scenarios (i.e., with different ratios of objects of two grasp types). The proposed algorithm is shown to reduce the number of grasping actions (i.e., enlarge the grasping efficiency, with maximum values of 161.0% in simulations, and 153.5% in real-world experiments) compared to single grasping modes.

Abstract Lesamnta‐LW‐BC is the internal block cipher of the Lesamnta‐LW lightweight hash function, specified in ISO/IEC 29192‐5:2016. It is based on the unbalanced Feistel network and Advanced Encryption Standard round function. In this study, the security of Lesamnta‐LW‐BC against integral and impossible‐differential attacks is evaluated. Specifically, the authors searched for the integral distinguishers and impossible differentials with Mixed‐Integer Linear Programming‐based methods. As a result, the discovered impossible differential can reach up to 21 rounds, while three integral distinguishers reaching 18, 19 and 25 rounds are obtained, respectively. Moreover, it is also feasible to construct a 47‐round integral distinguisher in the known‐key setting. Finally, a 20‐round key‐recovery attack is proposed based on the discovered 18‐round integral distinguisher and a 19‐round key‐recovery attack using a 17‐round impossible differential. To the best of the authors' knowledge, this is the first third‐party cryptanalysis of Lesamnta‐LW‐BC.

This paper presents distinguishing and key recovery attacks on the reduced-round SNOW-V and SNOW-Vi, which are stream ciphers proposed for standard encryption schemes for the 5G mobile communication system. First, we construct a Mixed-Integer Linear Programming (MILP) model to search for integral characteristics using the division property, and find the best integral distinguisher in the 3-, 4-, 5-round SNOW-V, and 5-round SNOW-Vi with time complexities of 28, 216, 248, and 216, respectively. Next, we construct a bit-level MILP model to efficiently search for differential characteristics, and find the best differential characteristics in the 3- and 4-round versions. These characteristics lead to the 3-round differential distinguishers for SNOW-V and SNOW-Vi with time complexities of 217 and 212 and the 4-round differential distinguishers for SNOW-V and SNOW-Vi with time complexities of 297 and 239, respectively. Then, we consider single-bit and dual-bit differential cryptanalysis, which is inspired by the existing study on Salsa and ChaCha. By carefully choosing the IV values and differences, we can construct practical bit-wise differential distinguishers for the 4-round SNOW-V, 4-, and 5-round SNOW-Vi with time complexities of 24.466, 21.000, and 214.670, respectively. Finally, we improve the existing differential attack based on probabilistic neutral bits, which is also inspired by the existing study on Salsa and ChaCha. As a result, we present the best key recovery attack on the 4-round SNOW-V and SNOW-Vi with time complexities of 2153.97 and 2233.99 and data complexities of 226.96 and 219.19, respectively. Consequently, we significantly improve the existing best key recovery attack in the initialization phase by the designers.

This paper presents three attack vectors of bit-wise cryptanalysis including rotational, bit-wise differential, and zero-sum distinguishing attacks on the AND-RX permutation Friet-PC, which is implemented in a lightweight authenticated encryption scheme Friet. First, we propose a generic procedure for a rotational attack on AND-RX cipher with round constants. By applying the proposed attack to Friet-PC, we can construct an 8-round rotational distinguisher with a time complexity of 2102. Next, we explore single- and dual-bit differential biases, which are inspired by the existing study on Salsa and ChaCha, and observe the best bit-wise differential bias with 2−9.552. This bias allows us to practically construct a 9-round bit-wise differential distinguisher with a time complexity of 220.044. Finally, we construct 13-, 15-, and 17-round zero-sum distinguishers with time complexities of 231, 263, and 2127, respectively. To summarize our study, we apply three attack vectors of bit-wise cryptanalysis to Friet-PC and show their superiority as effective attacks on AND-RX ciphers.