Redirigiendo al acceso original de articulo en 16 segundos...
Inicio  /  Algorithms  /  Vol: 16 Par: 9 (2023)  /  Artículo
ARTÍCULO
TITULO

Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization

Niousha Ghannad and Kalpdrum Passi    

Resumen

Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger population. Consequently, this has facilitated the ease with which counterfeiters can manipulate images. Convolutional neural network (CNN)-based feature extraction and detection techniques were used to carry out this task, which aims to identify the variations in image features between modified and non-manipulated areas. However, the effectiveness of the existing detection methods could be more efficient. The contributions of this paper include the introduction of a segmentation method to identify the forgery region in images with the U-Net model?s improved structure. The suggested model connects the encoder and decoder pipeline by improving the convolution module and increasing the set of weights in the U-Net contraction and expansion path. In addition, the parameters of the U-Net network are optimized by using the grasshopper optimization algorithm (GOA). Experiments were carried out on the publicly accessible image tempering detection evaluation dataset from the Chinese Academy of Sciences Institute of Automation (CASIA) to assess the efficacy of the suggested strategy. The results show that the U-Net modifications significantly improve the overall segmentation results compared to other models. The effectiveness of this method was evaluated on CASIA, and the quantitative results obtained based on accuracy, precision, recall, and the F1 score demonstrate the superiority of the U-Net modifications over other models.

 Artículos similares

       
 
Daniel Rusche, Nils Englert, Marlen Runz, Svetlana Hetjens, Cord Langner, Timo Gaiser and Cleo-Aron Weis    
Background: In this study focusing on colorectal carcinoma (CRC), we address the imperative task of predicting post-surgery treatment needs by identifying crucial tumor features within whole slide images of solid tumors, analogous to locating a needle in... ver más
Revista: Applied Sciences

 
Bochen Duan, Shengping Wang, Changlong Luo and Zhigao Chen    
In recent years, the surge in marine activities has increased the frequency of submarine pipeline failures. Detecting and identifying the buried conditions of submarine pipelines has become critical. Sub-bottom profilers (SBPs) are widely employed for pi... ver más

 
Yang Zhang, Yuan Feng, Shiqi Wang, Zhicheng Tang, Zhenduo Zhai, Reid Viegut, Lisa Webb, Andrew Raedeke and Yi Shang    
Waterfowl populations monitoring is essential for wetland conservation. Lately, deep learning techniques have shown promising advancements in detecting waterfowl in aerial images. In this paper, we present performance evaluation of several popular superv... ver más
Revista: Information

 
Fukuharu Tanaka, Teruhiro Mizumoto and Hirozumi Yamaguchi    
Advances in image analysis and deep learning technologies have expanded the use of floor plans, traditionally used for sales and rentals, to include 3D reconstruction and automated design. However, a typical floor plan does not provide detailed informati... ver más
Revista: Information

 
Youngkwang Kim, Woochan Kim, Jungwoo Yoon, Sangkug Chung and Daegeun Kim    
This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it... ver más
Revista: Information