Image Forgery Detection

EasyChair Preprint 12742

Abstract

Digital image forgery constitutes the deceptive manipulation of digital images to obscure or alter significant data within the image, often making it arduous to discern the manipulated regions from the original content. Preserving the integrity and authenticity of images necessitates the detection of such forgeries. The contemporary lifestyle, coupled with advancements in photography technology, has facilitated the ease of digital image manipulation through readily available image editing software. Consequently, the imperative to detect and mitigate image forgery operations has become paramount. Detection of image forgery encompasses various techniques, including but not limited to identifying object removal, object addition, and anomalous size alterations within the image. Images serve as potent means of communication, underscoring the critical importance of ensuring their veracity. In this project, a comprehensive approach is adopted, employing sophisticated algorithms such as Copy-Move Detection, Canny Edge Detection, Structure Similarity Index, Hierarchical Agglomerative Clustering, and Neural Network algorithms. These methodologies are leveraged to enhance accuracy in the detection and mitigation of digital image forgeries, thereby safeguarding

Keyphrases: Deep Image Forgery, Image forgery detection, Meachine Learning

@booklet{EasyChair:12742,
  author    = {Sura Sreeja Reddy and Gayam Santhosh Reddy and Pasupuleti Aashrithapriya and Arigela Rahulkumar and Pathan Bilalkhan},
  title     = {Image Forgery Detection},
  howpublished = {EasyChair Preprint 12742},
  year      = {EasyChair, 2024}}