Diffusion models has threatened image authenticity by enabling highly realistic fakes. Proactive defense offers protection by adding a "protective layer" that resists such manipulation. However, current proactive defenses mainly focus on text-guided editing but are less effective for the more challenging exemplar-guided tasks. To bridge this gap, we propose Proactive Spatial-Frequency Defense (PSFD), a novel proactive defense for exemplar-guided image editing. PSFD leverages adversarial attack to add subtle perturbations to make images immune to editing. We apply protections in both the frequency and spatial domains. Spatial perturbation disrupts feature extraction by forcing visual encoders to map the image to "bad" representations. Frequency perturbation tweaks the high-frequency components to distort the image’s texture information. We design two optimization strategies: PSFD-U that aims to generate maximal variation and PSFD-T that seeks to achieve specific editing styles. Extensive experiments on MS-COCO and ImageNet demonstrate PSFD’s strong defensive capabilities and transferability.

Spherical tensegrity structure has good dynamic stability, support strength and flexibility, and is widely used in the field of mobile robot research. Most of the tensegrity spherical robots deform themselves to make gravity work to realize the motion, but the deformation of both rods and ropes affects the robot's motion efficiency and motion instability. In this paper, a new type of tensegrity spherical robot is proposed, which is powered by six fixed ducted thrusters, and the thrust is provided to induce the robot to roll when in different attitudes. This paper firstly introduces the structural design and principle of the robot. Secondly analyzes the magnitude of the propulsive force required for the robot's motion and establishes a kinematic model. Finally, the robot prototype model was built and the robot motion experiments were conducted in simulation and the real environment respectively. The experimental results show that the robot has a simple structure but high motion efficiency, and has a strong ability to adapt to the environment.

Soft grippers conform to the shape and surface properties of the objects to be grasped, effectively avoiding damage to soft and fragile items. Despite the variety of existing soft gripper designs, their structures lack sufficient flexibility for effectively grasping slender objects or operating in narrow spaces. To address these challenges, we propose a soft gripper with single-finger grasping capabilities, inspired by the structure of crab claws. The structural design and the fabrication method of the gripper are introduced, and the analytical bending model is derived. Experiments are conducted under typical operating conditions to validate the model, and the results indicate that the measured data are in good accordance with the predicted responses. Furthermore, a series of grasping experiments are carried out to test the single-finger grasping capabilities of the proposed soft gripper. The results indicate that the proposed soft gripper can efficiently and stably grasp slender or irregular objects with a single finger. In particular, it demonstrates suitability for operations in narrow spaces and shows potential for handling complex tasks. This innovative design effectively reduces the complexity of the system, while exhibiting promising capabilities in grasping slender or irregular objects and operating within restricted spaces.

Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit anatomy and topology priors to directly expand sparse annotations into pseudo-labels. However, due to lack of attention to the ambiguous boundaries in medical images and insufficient exploration of sparse supervision, existing approaches tend to generate erroneous and overconfident pseudo proposals in noisy regions, leading to cumulative model error and performance degradation. In this work, we propose a novel WSS approach, named ProCNS, encompassing two synergistic modules devised with the principles of progressive prototype calibration and noise suppression. Specifically, we design a Prototype-based Regional Spatial Affinity (PRSA) loss to maximize the pair-wise affinities between spatial and semantic elements, providing our model of interest with more reliable guidance. The affinities are derived from the input images and the prototype-refined predictions. Meanwhile, we propose an Adaptive Noise Perception and Masking (ANPM) module to obtain more enriched and representative prototype representations, which adaptively identifies and masks noisy regions within the pseudo proposals, reducing potential erroneous interference during prototype computation. Furthermore, we generate specialized soft pseudo-labels for the noisy regions identified by ANPM, providing supplementary supervision. Extensive experiments on six medical image segmentation tasks involving different modalities demonstrate that the proposed framework significantly outperforms representative state-of-the-art methods.

Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available at https://github.com/llmir/FedLPPA.

Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid members and flexible cables, which has the advantages of simple structure, good flexibility, deformability, and impact resistance. Inspired by this structure, we design a novel worm-like tensegrity robot for different pipe environments, which can be manufactured rapidly at low cost. Firstly, a robotic module based on the tensegrity structure is designed inspired by the motion patterns of worm-like organisms. Then, the design process of the module is presented based on the mathematical analysis of the deformation. Finally, a prototype of the tensegrity robot is developed using simple and low-cost parts in less than an hour. To test the motion performance, load performance, and inspection capability of the tensegrity robot, we designed a series of experiments on horizontal pipes, vertical pipes, elbows, and steel pipes. Experimental results show that the worm-like tensegrity robot is simple in structure, easy to manufacture, low in cost, and good in performance.

This paper presents a novel concept to develop robots capable of crawling in tubular environments, inspired by the movement of earthworms and the biological musculoskeletal systems in nature. A tensegrity structures-based robotic module with shape changeability actuated by only one linear actuator is proposed. The mechanical structure of the robotic module is determined on the basis of force density method. By serially cascading three uniform modules, the in-pipe crawling robot is designed and manufactured. The robot has the abilities to crawl in both horizontal and vertical pipes with different inner diameters, and to pass through elbow pipes adaptively under the control of a simple actuation sequence. The effectiveness of the robot is demonstrated by experimental results on the prototype. Compared with existing robots, this proposed approach enables compact yet robust structures, along with enhanced compliance, mobility, and adaptability.

The bones, muscles, tendons and connective tissues form a continuous tension network throughout human body. This heterogeneous mixture presents the characteristics of tensegrity, providing the body with structurally integrity, stability and flexibility. Inspired by this, this paper proposes a novel soft robotic gripper based on tensegrity structures. Firstly, the design and working principle of the tensegrity-based robotic gripper is introduced, which is composed of a series of discrete rigid segments connected with tensegrity joints by means of tensional cables. Then, the kinematics of the robotic gripper is analyzed using force density method to obtain the relationship between the pose of the gripper and the tension of cables. Finally experiments on the developed prototype demonstrates that the robotic gripper is able to grasp various objects of different sizes, shapes, and materials. Additional desirable properties are derived from using tensegrity structures in the robotic gripper: light weight, high compliance, inherent safety, low cost, and waterproof and dustproof performance. It is suggested that tensegrity structures have great potential to be an effective alternative to the development of soft robotic grippers.