Robust Near Duplicate Image Matching for Digital Image Forensics

Abstract

Local invariant key point extraction has recently emerged as an attractive approach for detecting near duplicate images. Near duplicate images can be: (i) perceptually identical images (e.g. allowing for change in color balance, change in brightness, compression artifacts, contrast adjustment, rotation, cropping, filtering, scaling etc.), (ii) images of the same 3D scene (from different viewpoints). The requirements for identifying near duplicate images vary according to the application. In this paper we focus on image matching strategy that will assist in the detection of forged (copy-paste forgery) images. So far, no specific image matching strategy exists for this application. The state of the art methodologies tend to generate many false positives. In this paper we have introduced a novel matching strategy for pattern matching of key point distributions. Typical experiments conducted with real world images demonstrate success in near duplicate image retrieval for the application of digital image forensic. Proposed method outperforms some of the existing methods and is computationally efficient.

Improved Face Recognition Method usingPCA.

“…A feature is considered as matched with another feature when the distance to that feature is less than a specific fraction of the distance to the next nearest feature. Further spatial topology is verified by Angle -Line Ratio (ALR) statistics [15] among the matched feature distributions. This ensures that we reduce the number of false matches.…”

Near Duplicate Image Retrieval using Multilevel Local and Global Convolutional Neural Network Features

“…Performance improvement is found due to obtaining CNN for all selected local blocks as well as global (entire image). In order to measure performance of our model, mean average precision (mAP) is used and is calculated as shown in (4) where ri , N and M represents rank of i th retrieved image, number of relevant images and total number of queries respectively.…”

“…Defining near duplicates is a subjective matter. Detection of near duplicates has found many applications including copyright infringements [3], digital forgery [4], fraud detection [5], etc. Retrieving non identical images is found to be difficult.…”

Face Identification from Manipulated Facial Images Using SIFT

“…A feature is considered as matched with another feature when the distance to that feature is less than a specific fraction of the distance to the next nearest feature. Further spatial topology is verified by Angle-Line Ratio (ALR) statistics [10] among the matched feature distributions. This ensures that we reduce the number of false matches.…”