Scholarlink

<p>This study proposes a hybrid deep learning framework for detecting face morphing and swapping attacks using texture descriptors and advanced CNN classifiers. The process begins with the Viola-Jones Haar cascade, a widely used face detection method that effectively identifies and isolates facial regions. Subsequently, Local Binary Patterns (LBP) and Gabor filters are applied to extract texture-based features from these facial regions. Finally, the classification stage employs Simple Convolutional Neural Networks (CNN), Custom CNN and ResNet-152 to effectively distinguish between authentic and tampered images. We integrate LBP and Gabor filters to extract robust texture features and classify them using a combination of Simple CNN, Custom CNN, and ResNet-152. Our approach achieves an ACER of 2.82% on the DEFACTO dataset, outperforming several state-of-the-art methods. Additionally, the paper presents ablation studies that confirm the complementary benefits of LBP and Gabor features in enhancing detection performance and benchmarking against five state-of-the-art methods, followed by a detailed analysis of model performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hybrid CNN-based detection of forged facial images using Gabor filters and local binary patterns

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月30日

查看全文
  • Shilpa Kaman,
  • Syeda Bibi Javeriya,
  • Aziz Makandar

摘要

This study proposes a hybrid deep learning framework for detecting face morphing and swapping attacks using texture descriptors and advanced CNN classifiers. The process begins with the Viola-Jones Haar cascade, a widely used face detection method that effectively identifies and isolates facial regions. Subsequently, Local Binary Patterns (LBP) and Gabor filters are applied to extract texture-based features from these facial regions. Finally, the classification stage employs Simple Convolutional Neural Networks (CNN), Custom CNN and ResNet-152 to effectively distinguish between authentic and tampered images. We integrate LBP and Gabor filters to extract robust texture features and classify them using a combination of Simple CNN, Custom CNN, and ResNet-152. Our approach achieves an ACER of 2.82% on the DEFACTO dataset, outperforming several state-of-the-art methods. Additionally, the paper presents ablation studies that confirm the complementary benefits of LBP and Gabor features in enhancing detection performance and benchmarking against five state-of-the-art methods, followed by a detailed analysis of model performance.