Scholarlink

<p>The advancements of technology in every domain are leading to the misuse of data, especially in information sharing. Many ways have been introduced for information sharing in the digital world, and digital images act as a powerful way to share information. With the development of image editing software and the availability of mobile devices, the manipulation of images is very easy, and it is challenging to identify the manipulated images. People mainly use forged images to spread rumours and can damage the public trust in media; hence, it is necessary to develop an effective forgery detection task. Various researchers, image forensic experts, have been working on image forgery detection and have identified some issues, including high computational complexity, inaccurate classification, and a lack of depth feature analysis. So the proposed approach is intended to develop a novel physics based forgery detection approach with efficient feature extraction. The digital image’s shadow features, handcrafted and deep features, were extracted using Hough Transform, Sobel Operator, and ResNet-151 module, respectively. These extracted multi-features are fused using a novel Squeeze Excitation based Vision Transformer Network (SE-ViTN) for better feature embedding representations. Furthermore, the embedded feature patches are classified as normal or forged using an Optimized Radial Basis Support Vector Machine (ORB-SVM) classifier, and their parameters are tuned using the Secretary Bird Optimization Algorithm (SBOA), which reduces the complexity and enhances efficiency. This research is validated on two benchmark datasets, namely DSO-1 and the OIS dataset. Further, experimental results showed better performance accuracy of 98.83 and 97.75% for both datasets, respectively.</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

Physics based forgery detection using radial basis support vector machine with multi-feature fusion

  • 期刊论文

  • 发表日期：

    2026年2月17日

查看全文
  • Divya P. Surve,
  • Anant V. Nimkar

摘要

The advancements of technology in every domain are leading to the misuse of data, especially in information sharing. Many ways have been introduced for information sharing in the digital world, and digital images act as a powerful way to share information. With the development of image editing software and the availability of mobile devices, the manipulation of images is very easy, and it is challenging to identify the manipulated images. People mainly use forged images to spread rumours and can damage the public trust in media; hence, it is necessary to develop an effective forgery detection task. Various researchers, image forensic experts, have been working on image forgery detection and have identified some issues, including high computational complexity, inaccurate classification, and a lack of depth feature analysis. So the proposed approach is intended to develop a novel physics based forgery detection approach with efficient feature extraction. The digital image’s shadow features, handcrafted and deep features, were extracted using Hough Transform, Sobel Operator, and ResNet-151 module, respectively. These extracted multi-features are fused using a novel Squeeze Excitation based Vision Transformer Network (SE-ViTN) for better feature embedding representations. Furthermore, the embedded feature patches are classified as normal or forged using an Optimized Radial Basis Support Vector Machine (ORB-SVM) classifier, and their parameters are tuned using the Secretary Bird Optimization Algorithm (SBOA), which reduces the complexity and enhances efficiency. This research is validated on two benchmark datasets, namely DSO-1 and the OIS dataset. Further, experimental results showed better performance accuracy of 98.83 and 97.75% for both datasets, respectively.