Autoregressive-Elephant Herding Optimization based Generative Adversarial Network for copy-move forgery detection with Interval type-2 fuzzy clustering
作者：
Highlights：
• Autoregressive Elephant Herding Optimization based Generative Adversarial Network for forgery score detection.
• The proposed A-EHO algorithm trains the GAN.
• A-EHO à Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) with Elephant Herding Optimization (EHO).
• RideNN classifier detects the forgery image based on the feature vector and the forgery score.
摘要
•Autoregressive Elephant Herding Optimization based Generative Adversarial Network for forgery score detection.•The proposed A-EHO algorithm trains the GAN.•A-EHO à Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) with Elephant Herding Optimization (EHO).•RideNN classifier detects the forgery image based on the feature vector and the forgery score.
论文关键词：Copy-move forgery,Image forensics,Generative Adversarial Network (GAN),Rider Optimization Algorithm-based Neural Network (RideNN),Elephant Herding Optimization (EHO)
论文评审过程：Received 25 November 2021, Revised 9 April 2022, Accepted 27 May 2022, Available online 3 June 2022, Version of Record 21 July 2022.
论文官网地址：https://doi.org/10.1016/j.image.2022.116756
