Convolutional neural networks (CNNs) are known to be vulnerable to adversarial examples even in black-box scenarios, posing a significant threat to their reliability and security. Most existing black-box attack methods primarily focus on data-free scenarios, which often require a large number of queries and yield low attack success rates. But in practical applications, it is feasible to collect a small amount of data associated with the target network. In light of this, in this article, we propose an advancing few-shot black-box attack with alternating training scheme using few data and alternating training to improve the efficiency and attack success rate. Specifically, we propose an alternating training approach consisting of two parts, both aimed at optimizing the substitute network, which alternate and reinforce each other, leading to a significant reduction in the query budget required for a successful attack. In addition, we propose an image degradation (ID) module that expands the data volume diversity through ID techniques to mitigate the problem of generator overfitting. Furthermore, we design a model specific adapter to enable the substitute networks to dynamically adjust the parameters for different target networks. Extensive experiments demonstrate the efficacy of our approach in significantly reducing the query budget while achieving higher attack success rates compared to state-of-the-art competitors.

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.

The linear measuring range is limited by the relative magnetic permeability of magnetic fluid (MF) in tradition fiber magnetic field sensors utilizing MF refractive index modulation effect. The article proposes a fiber optic sensor with the help of MF magneto-shape effect, which can extend linear measuring range. The magneto-shape effect is not only related to the relative magnetic permeability of MF, but also to the magnetic field distribution on the concave, which can expand the linear measurement range of magnetic field. The sensing probe consists of single mode fiber (SMF)-air-MF-air-SMF. The measuring range of sensing structure can reach 402.1 Gs, the sensitivity is 180 pm/Gs from $0\sim 205.9$ Gs and 81 pm/Gs at $205.9\sim 402.1$ Gs. The response times of structure reach 50 ms and 137 ms at 45.5 Gs and 91.2 Gs, respectively. In addition, the linear measurement range of structure with magneto-shape effect is twice as high as that based on magneto-refractive index modulation effect under the premise of using the same MF. The sensing structure possesses the characteristics of fast response time, highly sensitive and large linear measurement range.