Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Abstract

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

Advancing Copy-Move Manipulation Detection in Complex Image Scenarios Through Multiscale Detector

“…Numerous researchers have investigated a block-based methodology for identifying copy-move manipulation. Some of these investigations comprise; Babu et al [11], proposed to utilize a blend of several iterations of Local Binary Patterns (LBP) such as local ternary pattern, local phase quantization, and local Gabor binary pattern. The characteristics of BP are employed to train the classifier model, while the Support Vector Machine is utilized for the classification of copy-move manipulation verification.…”

Data mining based framework for organisational financial management

“…Furthermore, Data mining technology has piqued the interest of well-known research institutions, leading to the development of intelligent decision analysis algorithms for financial management, as well as business-based concepts such as document warehouses, are proposedintelligence. Moreover, in the telecom sector, to help telecom operators with intelligent data analysis, a service-oriented business intelligence architecture based on the decision tree algorithm has been developed [4].…”

“…Conventional accounting methodology and examination of financial data approaches have failed to suit people's multifaceted needs in terms of form, timeliness, and effectiveness for analyzing and making decisions on complex problems. The emergence of artificial intelligence and expertise systems has provided excellent solutions to such difficulties, propelling the evolution of contemporary accounting systems has progressed from information and networkbased systems to intelligent systems [4]. The tripartite theory, which serves as the foundational theory of Decision Support Systems (DSS) and has significantly influenced the subsequent advancement of DSS, has contributed to this transition towards intelligent financial management systems.…”