Multi-robot swarms play an essential role in complex missions including battlefield reconnaissance, agricultural pest monitoring, as well as disaster search and rescue. Unfortunately, given the complexity of swarm algorithms, logical vulnerabilities are inevitable and often lead to severe safety and security consequences. Although various methods have been presented for detecting logical vulnerabilities through software testing, when they are used in swarm environments, these techniques face significant challenges: 1) Due to the swarm’s vast composable parameter space, it is extremely difficult to generate failure-triggering scenarios, which is crucial to effectively expose logical vulnerabilities; 2) Because of the swarm’s high flexibility and dynamism, it is challenging to model and evaluate the global swarm state, particularly in terms of cooperative behaviors, which makes it difficult to detect logical vulnerabilities.In this work, we propose RSFuzz, a robustness-guided swarm fuzzing framework designed to detect logical vulnerabilities in multi-robot systems. It leverages the robustness of behavioral constraints to quantitatively evaluate the swarm state and guide the generation of failure-triggering scenarios. In addition, RSFuzz identifies and targets key swarm nodes for perturbations, effectively reducing the input space. Upon the RSFuzz framework, we construct two swarm fuzzing schemes, Single Attacker Fuzzing (SA-Fuzzing) and Multiple Attacker Fuzzing (MA-Fuzzing), which employ single and multiple attackers, respectively, during fuzzing to disturb swarm mission execution. We evaluated RSFuzz’s performance with three popular swarm algorithms in simulated environments. The results show that RSFuzz outperforms the state-of-the-art with an average improvement of 17.75% in effectiveness and a 38.4% increase in efficiency. We also validated some detected vulnerabilities in real-world environments. Our code and data are publicly available.

Knowledge distillation aims to learn a small student model by leveraging knowledge from a larger teacher model. The gap between these heterogeneous models hinder their knowledge transfer and it would be more challenging when the teacher model is from another task. Previous methods view the teacher model as a perfect feature extractor and train the student model to mimic it. While we notice that the teacher model has defect in extracting features of another task samples. To improve knowledge distillation under such situation, we propose Multiple Auxiliary Subspaces (MAS). Most previous methods improve distillation performance by representation alignment, while we resort to the promotion of the teacher model which is more suitable for cross-task distillation. The MAS distills the knowledge in a mutual learning way based on an auxiliary network. Along with the training procedure, the teacher model is improved by the auxiliary network which works as a trainable part of the teacher model and learn the features distribution of target samples from the student model. And this promotion of the teacher model will benefit the student model via the following distillation procedure. We adopt the representation alignment technique, multiple auxiliary networks to further enhance the proposed method. The MAS works well with limited or sufficient labeled target data. If the source data is available, based on it the MAS can construct another auxiliary network to further improve the distillation. Experiments are conducted to validate that the MAS outperforms baseline methods and achieves state-of-the-art results on several standard benchmarks.

In recent years, single image dehazing methods based on Atmospheric Scattering Model (ASM) have achieved state-of-the-art results. But the dehazing outputs of those methods suffer from color shift and blocking effect. Our preliminary experiments show that the negative bias of the estimated transmission and the bias of tiny transmission value will cause serious color shift. Therefore, in this study, a new loss function (TransLoss) and a new natural activation function (NAF) are proposed to restrain negative bias of transmission and avoid tiny transmission value from being activated, respectively. Moreover, it is noted that the block effect is caused by patch-level transmission estimation mechanism in existing dehazing models. To address this issue, a new pixel-level transmission estimation module (ETM) is dedicated designed to avoid blocking effect. In the end, an end-to-end CNN dehazing network avoiding color shift and blocking effect is developed, termed as ABC-Net. Experimental results indicate that the ABC-Net outperforms four comparison methods on both synthetic and real-world images.

Single image dehazing is an ill-posed problem. Most existing works use the atmospheric scattering model (ASM) [1] and some natural priors to dehazing. Recently, DehazeNet [2] was developed using deep learning approach achieves the state-of-the-art results on many test hazy images, which motivates us to adopt the deep learning approach in this study. After carefully research on the essential principle of dehazing using ASM, we develop a novel end-to-end convolutional neural network to efficiently implement the inverse ASM for single image dehazing, which is termed as IASM -Net. Specifically, the ASM is equivalently decomposed into three sub-model and the IASM-Net is designed to model these sub-model efficiently. Our study shows that IASM-Net is jointly optimized by minimizing the model error between the output and the clear ground truth, which gives better performance compared existing CNN-based transmission model approach [1], [2]. To facilitate this study, a sufficient aerial image set containing more than 6K aerial photos is built to train and test our IASM-Net. Experimental results validate the effectiveness and efficiency of our well designed IASM -Net.