Due to the remarkable performance of multi-modal large language models (MLLMs) in multi-modal capabilities, multi-modal in-context learning (M-ICL) has garnered widespread attention for fast adapting MLLMs to downstream tasks. However, the vulnerability of M-ICL to attacks remains largely unexplored. In this work, we take the first step to explore the backdoor vulnerability of M-ICL, which allows the adversary only to manipulate the multi-modal demonstration examples to mislead the victim model. We propose a multi-modal backdoor strategy on M-ICL via cross-modal concept mis-matching under black-box attack setting. Extensive experimental results demonstrate that our attacks exhibit high attack effectiveness while preserving the normal functionality of the victim model. Moreover, we further conduct experiments to prove our attacks are robust against backdoor defenses and still remain effective in various real-world conditions.

With the widespread adoption of deep learning in image recognition, backdoor attacks have emerged as a significant security threat, drawing increasing attention from the research community. Traditional backdoor attacks are often limited to the digital domain, while few existing physical-world attacks suffer from a lack of stealthiness. In this paper, inspired by the natural defocus blur commonly caused by camera optics in real-world environments, we propose a physically-aware backdoor attack method called DBBA based on the defocus blur phenomenon. By leveraging Gaussian blur to simulate this natural phenomenon, the proposed method enhances both the stealthiness and plausibility of the trigger. To further optimize the attack effectiveness while maintaining stealthiness, we introduce a Particle Swarm Optimization (PSO) algorithm to automatically search for the optimal Gaussian blur parameters that best simulate the defocus phenomenon. We conduct extensive experiments on multiple mainstream image classification datasets and across various model architectures. Experimental results demonstrate that the proposed defocus-blur based trigger achieves a high attack effectiveness with minimal degradation in the classification accuracy of the model. In addition, evaluations against representative defense techniques reveal that the proposed method exhibits strong stealthiness and robustness.

Aiming at the emergency decision-making problem of major emergencies, this article proposes a large group emergency decision-making (LGEDM) approach with public opinions mining on hesitation fuzzy language term set (HFLTS). First, extract keywords that represent general preferences on events from the Weibo platform, classify keywords using the word similarity-based keyword clustering algorithm and identify decision attributes and their weights. Next, define the similarity measure and hesitation fuzzy entropy measure of HFLTS, quantify the decision risk of experts using the risk measurement model, and cluster all experts into several subgroups using the risk metric-based group clustering algorithm. Subsequently, assign clusters' weights on their risk value and size and obtain each cluster's preference matrix by the HIOWA operator. Finally, derive the ranking results of alternatives using the sorting process, and an example of “COVID-19” is presented to verify the rationality and effectiveness of the proposed method.

The Five-Year Plan (FYP) in China guides the social and economic development. The features of ecology-related vocabularies in national and regional FYPs help to study China's emphasis and focus on ecological civilization. In this paper, a multi-phrase dictionary with syntactic and semantic features is constructed to analyze the features and trends of ecology-related vocabularies through social network and phrase topic model. Then, based on studies on the 13th and 14th FYPs of China and 31 provinces and cities, it is found that remarkable different stage definitions and tasks of ecological civilization construction are proposed according to different description vocabularies, with more pragmatic measures to fulfill its “carbon” target in 2030 as a duty-bound responsibility and mission to cope with climate change. The research ideas and methods in this paper can provide references for similar policy text analysis, and the conclusions from empirical studies are helpful to grasp the focus and trends of ecological civilization-related policies in China and various provinces and cities.