Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques

Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.

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

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.