Image steganalysis has been a key technology in information security in recent years. However, existing methods are mostly limited to the binary classification for detecting steganographic images used in digital watermarking, privacy protection, illicit data concealment, and security images, such as unaltered cover images or surveillance images. They cannot identify the steganography algorithms used in steganographic images, which restricts their practicality. To solve this problem, this paper proposes a general steganography algorithms recognition scheme based on image big data matching with improved ResNet50. The scheme first intercepts the image region with the highest complexity and focuses on the key features to improve the analysis efficiency; subsequently, the original image of the image to be detected is accurately located by the image big data matching technique and the steganographic difference feature image is generated; finally, the ResNet50 is improved by combining the pyramid attention mechanism and the joint loss function, which achieves the efficient recognition of the steganography algorithm. To verify the feasibility and effectiveness of the scheme, three experiments are designed in this paper: verification of the selection of the core analysis region, verification of the image similarity evaluation based on Peak Signal-to-Noise Ratio ($PSNR$), and performance verification of the improved ResNet50 model. The experimental results show that the scheme proposed in this paper outperforms the existing mainstream steganalysis models, such as ZhuNet and YeNet, with a detection accuracy of 96.11%, supports the recognition of six adaptive steganography algorithms, and adapts to the needs of analysis of multiple sizes and image formats, demonstrating excellent versatility and application value.

To overcome the limitations of conventional robot task allocation algorithms, which often converge to suboptimal solutions, suffer from low computational accuracy, and produce excessively long execution paths as problem scales increase, we propose a novel marine predator algorithm (NMPA). By redesigning the predator–prey encoding mechanism in the traditional marine predator algorithm (MPA) and integrating information-based environmental search strategies with local search techniques, the proposed method substantially improves global exploration capability and solution precision. The NMPA is then evaluated on multiple benchmark and practical scenarios, including the traveling salesman problem (TSP), multi-traveling salesman problem (MTSP), single-robot task allocation with rigid time windows, and multi-robot task allocation. Experimental results on TSP and MTSP indicate that NMPA achieves more stable convergence, higher accuracy, and stronger robustness than ant colony optimization (ACO), simulated annealing, genetic algorithms (GAs), and their variants. Moreover, validation on real-world factory data in both single-robot and multi-robot task allocation scenarios confirms that NMPA delivers superior solution quality and overall performance compared with ACO, GAs, and their variants.