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<p>The increasing reliance on remote sensing imagery for crowd analysis necessitates accurate and efficient methods for density map generation and fake image detection. Traditional techniques often face challenges in handling complex crowd patterns, noise, and inconsistent data quality, leading to inaccuracies in density estimation and vulnerability to counterfeit imagery. To address these issues, this research introduces a novel hybrid nested deep convolutional generative adversarial network (NDCGAN) augmented with Block Attention Mechanisms. The proposed framework leverages the nested GAN architecture for robust feature extraction and integrates block attention modules to enhance spatial and channel-wise feature representation. This hybrid approach ensures the generation of high-quality density maps while simultaneously detecting fake images with improved precision. The primary objective is to enhance the reliability of crowd counting systems by delivering accurate density estimations and safeguarding against manipulated imagery. Experimental evaluations demonstrate significant improvements in accuracy, robustness, and computational efficiency compared to conventional methods, showcasing its potential for real-world applications in crowd management and security using remote sensing imagery.</p>

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Designing Hybrid Nested GAN with Block Attention Mechanisms for Accurate Crowd Density Mapping and Fake Image Detection Using Remote Sensor Imaging

  • 期刊论文

  • 发表日期：

    2025年2月19日

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  • B. Ganga,
  • B. T. Lata,
  • K. R. Venugopal

摘要

The increasing reliance on remote sensing imagery for crowd analysis necessitates accurate and efficient methods for density map generation and fake image detection. Traditional techniques often face challenges in handling complex crowd patterns, noise, and inconsistent data quality, leading to inaccuracies in density estimation and vulnerability to counterfeit imagery. To address these issues, this research introduces a novel hybrid nested deep convolutional generative adversarial network (NDCGAN) augmented with Block Attention Mechanisms. The proposed framework leverages the nested GAN architecture for robust feature extraction and integrates block attention modules to enhance spatial and channel-wise feature representation. This hybrid approach ensures the generation of high-quality density maps while simultaneously detecting fake images with improved precision. The primary objective is to enhance the reliability of crowd counting systems by delivering accurate density estimations and safeguarding against manipulated imagery. Experimental evaluations demonstrate significant improvements in accuracy, robustness, and computational efficiency compared to conventional methods, showcasing its potential for real-world applications in crowd management and security using remote sensing imagery.