An Efficient and Fast Digital Image Copy-Move Forensic Technique

Khudhur, Mokhles Hussein; Waleed, Jumana; Hatem, Hiyam; Abduldaim, Areej M.; Abdullah, Dhahir Abdulhade

doi:10.1109/icets.2018.8724611

Cited by 4 publications

(1 citation statement)

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“…This model is limited in that it uses a large number of parameters, which makes it difficult to train the dataset. Khudhur et al [14] developed a method for detecting region duplication forgery. Each pixel of the luminance part of the image is divided into blocks of overlapping pixels.…”

Section: Spatial Domain Forgery Detectionmentioning

confidence: 99%

A survey on passive digital video forgery detection techniques

Jegaveerapandian,

Rani,

Periyaswamy

et al. 2023

IJECE

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Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of this paper is to provide a comprehensive review of the passive methods for detecting video forgeries. This survey has the primary goal of studying and analyzing the existing passive techniques for detecting video forgeries. First, an overview of the basic information needed to understand video forgery detection is presented. Later, it provides an in-depth understanding of the techniques used in the spatial, temporal, and spatio-temporal domain analysis of videos, datasets used, and their limitations are reviewed. In the following sections, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed in more depth. Finally, identifying loopholes in existing surveys so detecting forged videos much more effectively in the future are discussed.

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Section: Spatial Domain Forgery Detectionmentioning

confidence: 99%