With the rapid evolution of deepfake technologies and the wide dissemination of digital media, personal privacy is facing increasingly serious security threats. Deepfake proactive forensics, which involves embedding imperceptible watermarks to enable reliable source tracking, serves as a crucial defense against these threats. Although existing methods show strong forensic ability, they rely on an idealized assumption of single watermark embedding, which proves impractical in real-world scenarios. In this paper, we formally define and demonstrate the existence of Multi-Embedding Attacks (MEA) for the first time. When a previously protected image undergoes additional rounds of watermark embedding, the original forensic watermark can be destroyed or removed, rendering the entire proactive forensic mechanism ineffective. To address this vulnerability, we propose a general training paradigm named Adversarial Interference Simulation (AIS). Rather than modifying the network architecture, AIS explicitly simulates MEA scenarios during fine-tuning and introduces a resilience-driven loss function to enforce the learning of sparse and stable watermark representations. Our method enables the model to maintain the ability to extract the original watermark correctly even after a second embedding. Extensive experiments demonstrate that our plug-and-play AIS training paradigm significantly enhances the robustness of various existing methods against MEA.

The object detection of the substation is the key to ensuring the safety and reliable operation of the substation. The traditional image detection algorithms use the corresponding texture features of single-class objects and would not handle other different class objects easily. The object detection algorithm based on deep networks has generalization, and its sizeable complex backbone limits the application in the substation monitoring terminals with weak computing power. This article proposes a multitargets joint training lightweight model. The proposed model uses the feature maps of the complex model and the labels of objects in images as training multitargets. The feature maps have deeper feature information, and the feature maps of complex networks have higher information entropy than lightweight networks have. This article proposes the heat pixels method to improve the adequate object information because of the imbalance of the proportion between the foreground and the background. The heat pixels method is designed as a kind of reverse network calculation and reflects the object's position to the pixels of the feature maps. The temperature of the pixels indicates the probability of the existence of the objects in the locations. Three different lightweight networks use the complex model feature maps and the traditional tags as the training multitargets. The public dataset VOC and the substation equipment dataset are adopted in the experiments. The experimental results demonstrate that the proposed model can effectively improve object detection accuracy and reduce the time-consuming and calculation amount.

For autonomous robots, the Fuzzy C-means algorithm (FCM) is used in the tasks like self-position estimation, path planning and environment navigation. This paper proposes a suspect point recheck method for fuzzy clustering algorithm. First, the proposed method works as the typical FCM to obtain an original clustering result. Then the method classifies all the data points into normal points and suspect points according to their memberships of each cluster. Finally, the method redistributes the suspect points according to the information of their nearby normal points. Three datasets from UCI Machine Learning Repository are used in the experiments. The experimental results verify that the proposed method has higher clustering capability.

For a software development project, management often faces the dilemma of when to stop testing the software and release it for operation, which requires careful decision making as it has great impact on both software reliability and project cost. In most existing research on the optimal software release problem, the cost considered was the Expected Cost (EC) of the project. However, what concerns management is the Actual Cost (AC) of the project rather than the EC. Treatment (such as minimization) of the EC may not ensure the desired low level of the AC due to the uncertainty (variability) involved in the AC. In this paper, we study the uncertainty in software cost and its impact on optimal software release time in detail. The uncertainty is quantified by the variance of the AC and several risk functions. A risk-control approach to the optimal software release problem is proposed. New formulations of the problem which are extensions of current formulations are developed and solution procedures are established. Several examples are presented. Results reveal that it seems crucial to take into account the uncertainty in software cost in the optimal software release problem; otherwise, unsafe decisions may be reached which could be a false dawn to management.