Forensic Science Research Summary for Forgery Detection of Digital Images

Abstract

An important measure of proof collection, storage, and authentication in forensic sciences, which decide the safety and security of any system documents, which can be either portable document formats or scanned images. To gather evidence, or plan a forensic investigation digital images are secured with different modern methodologies. Digital image analysis includes image recovery and surveillance for image information improvement. The goal of forgery detection is to maximize the extraction of information from manipulated images, particularly noisy and post-processed images. Because digital image processing is becoming popular with many advantages in scientific and engineering applications, the forgery techniques are also growing at a rapid rate. Therefore, the main focus is on different types of forgery detection in digital image processing with the help of all transform techniques and comparing their best results for further improvement in order to generate a new approach for a future forensic science investigation.

Document Image Forgery and Detection Methods Using Image Processing Techniques – A Review

“…The proposed system uses an SVM decision designed to indicate the probability that two documents belong to the same author. Monika et al [7] explains that the purpose of counterfeit detection is to maximize the extraction of information from manipulated images, especially noisy post-processed images. Therefore, the main focus is to compare and further improve various types of counterfeit detection in digital image processing using all conversion techniques and their best results, creating a new approach for future forensic research.…”

“…Using computer programming skills, forensic image processing is a novel method for enhancing digital photos from surveillance, closed-circuit TV, and many other applications. Digital filters used in these systems can block a variety of noise, including Gblur noise, Pepper noise, Salt noise, Gaussian White Noise, Motion Noise, Multiplicative Spectrum, Poisson, and Filter Dilation, among others [7].…”