Scholarlink

<p>The rapid rise of manipulated videos in the digital era necessitates advanced forgery detection and localization techniques. This study presents a novel approach using a GAN-based framework integrated with a 3D Swin-B Transformer Encoder–Decoder architecture for future frame prediction. A ConvLSTM-based Siamese network serves as a discriminator, capturing spatiotemporal inconsistencies, while forgery localization is enhanced through attention maps and a patch-based strategy. Experimental validation on diverse datasets demonstrates the effectiveness of the proposed method, achieving 98.97% detection accuracy and 100% localization accuracy, surpassing existing techniques. The results highlight the robustness of the approach in identifying and localizing various types of video forgeries, making a significant contribution to digital content forensics.</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

Detecting and localizing multiple forgeries with TransGAN

  • 期刊论文

  • 发表日期：

    2025年4月3日

查看全文
  • Upasana Singh,
  • Sandeep Rathor,
  • Manoj Kumar

摘要

The rapid rise of manipulated videos in the digital era necessitates advanced forgery detection and localization techniques. This study presents a novel approach using a GAN-based framework integrated with a 3D Swin-B Transformer Encoder–Decoder architecture for future frame prediction. A ConvLSTM-based Siamese network serves as a discriminator, capturing spatiotemporal inconsistencies, while forgery localization is enhanced through attention maps and a patch-based strategy. Experimental validation on diverse datasets demonstrates the effectiveness of the proposed method, achieving 98.97% detection accuracy and 100% localization accuracy, surpassing existing techniques. The results highlight the robustness of the approach in identifying and localizing various types of video forgeries, making a significant contribution to digital content forensics.