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In the face of an increasingly intricate network structure and a multitude of security threats, cyber deception defenders often employ deception assets to safeguard critical real assets. However, when it comes to the intranet lateral movement attackers in the cyber kill chain, the deployment of deception assets confronts the challenges of lack of dynamics, inability to make real-time decisions, and not considering the dynamic change of an attacker's strategy. To address these issues, this study introduces a novel maze pathfinding model tailored to the lateral movement context, in which we try to find out the attacker's location to deploy deception assets accurately for interception. The attack–defense process is modeled as a multi-agent stochastic game, by comparing it with random action policy and Minimax-Q algorithm, we choose Nash Q-learning to solve the deception asset's deployment strategy to achieve the optimal solution effect. Extensive simulation tests reveal that our proposed model exhibits good convergence properties. Moreover, the average defense success rate surpasses 70%, attesting to the model's efficacy. [ABSTRACT FROM AUTHOR]

With the rise of image manipulation techniques, an increasing number of individuals find it easy to manipulate image content. Undoubtedly, this presents a significant challenge to the integrity of multimedia data, thereby fueling the advancement of image forgery detection research. A majority of current methods employ convolutional neural networks (CNNs) for image manipulation localization, yielding promising outcomes. Nevertheless, CNN-based approaches possess limitations in establishing explicit long-range relationships. Consequently, addressing the image manipulation localization task necessitates a solution that adeptly builds global context while preserving a robust grasp of low-level details. In this paper, we propose GPNet to address this challenge. GPNet combines Transformer and CNN in parallel which can build global dependency and capture low-level details efficiently. Additionally, we devise an effective fusion module referred to as TcFusion, which proficiently amalgamates feature maps generated by both branches. Thorough extensive experiments conducted on diverse datasets showcase that our network outperforms prevailing state-of-the-art manipulation detection and localization approaches. [ABSTRACT FROM AUTHOR]

Fabric inspection is critical in fabric manufacturing. Automatic detection of fabric defects in the textile industry has always been an important research field. Previously, manual visual inspection was commonly used; however, there were drawbacks such as high labor costs, slow detection speed, and high error rates. Recently, many defect detection methods based on deep learning have been proposed. However, problems need to be solved in the existing methods, such as detection accuracy and interference of complex background textures. In this paper, we propose an efficient segmentation algorithm that combines traditional operators with deep learning networks to alleviate the existing problems. Specifically, we introduce a Gabor filter into the model, which provides the unique advantage of extracting low-level texture features to solve the problem of texture interference and enable the algorithm to converge quickly in the early stages of training. Furthermore, we design a U-shaped architecture that is not completely symmetrical, making model training easier. Meanwhile, multi-stage result fusion is proposed for precise location of defects. The design of this framework significantly improves the detection accuracy and effectively breaks through the limitations of transformer-based models. Experimental results show that on a dataset with one class, a small amount of data, and complex sample background texture, our method achieved 90.03% and 33.70% in ACC and IoU, respectively, which is almost 10% higher than other previous state of the art models. Experimental results based on three different fabric datasets consistently show that the proposed model has excellent performance and great application potential in the industrial field. [ABSTRACT FROM AUTHOR]