Hybrid Image Splicing Detection: A Novel Approach Using CLAHE, Improved CNN, and SVM

Strategic Insights -

A team of researchers from Chitkara University has developed a novel hybrid method for detecting image splicing forgery (ISF) that integrates an improved convolutional neural network (CNN), support vector machine (SVM) classifier, and contrast-limited adaptive histogram equalization (CLAHE). The proposed methodology employs CLAHE to enhance the extraction of hidden features that forgery has obscured, and the improved CNN uses sophisticated feature extraction techniques to achieve superior classification accuracy. The SVM classifier is incorporated due to its exceptional processing speed and efficiency. The research aims to address the constraints of current deep learning models in terms of computational efficiency and accuracy, thereby demonstrating substantial enhancements in performance metrics for image splicing forgery detection (ISFD).

Key Takeaways:

  • The proposed hybrid framework integrates CLAHE, improved CNN, and SVM to detect image splicing forgery (ISF) with high accuracy.
  • The research aims to address the constraints of current deep learning models in terms of computational efficiency and accuracy.
  • The proposed methodology employs CLAHE to enhance the extraction of hidden features that forgery has obscured.
  • The improved CNN uses sophisticated feature extraction techniques to achieve superior classification accuracy.
  • SVM is incorporated due to its exceptional processing speed and efficiency.
  • The research concludes that the proposed system effectively differentiates between authentic and manipulated images.
  • The study highlights the importance of developing novel methods to address the challenges of image splicing forgery detection.
  • The research was conducted by a team from Chitkara University, School of Engineering and Technology, Baddi, Himachal Prades, India.

Statistics:

  • The proposed system achieved an accuracy of 95% in detecting image splicing forgery (ISF) (Source: Hybrid Image Splicing Detection: Integrating Clahe, Improved Cnn, and Svm for Digital Image Forensics).
  • The research cited in Expert Systems with Applications (2025;273) highlights the increasing accessibility of image editing applications, resulting in a surge in amateur image manipulation.
  • The improved CNN employed in the proposed methodology uses sophisticated feature extraction techniques to achieve superior classification accuracy.

Sources:

  • Hybrid Image Splicing Detection: Integrating Clahe, Improved Cnn, and Svm for Digital Image Forensics. Expert Systems With Applications, 2025;273.
  • Expert Systems with Applications can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Expert Systems With Applications - www.journals.elsevier.com/expert-systems-with-applications/)
  • Navneet Kaur, Chitkara University, School of Engineering and Technology, Baddi, Himachal Prades, India.