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.

A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques

Digital images can be tampered easily with simple image editing software tools.Therefore,image forensic investigation on the authenticity of digital images’content is increasingly important.Copy-move is one of the most common types of image forgeries.Thus,an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper.These methods are classified into three types:block-based methods,keypoint-based methods,and deep learning-based methods.In addition,the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost.Finally,further research directions are discussed.

Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.

Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image forgery detection remains a challenging problem.For the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further improved.To the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected sub-images.Our method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery sub-images.Second,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be achieved.Finally,the dual feature matching is used to match the features and locate the forgery regions.What’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high robustness.Through experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.

An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks tohigh internet speed and high resolution, cheap and easily accessible digitalcameras. We generate, transmit and store millions of images every second.Most of these images are insignificant images containing only personal information.However, in many fields such as banking, finance, public institutions,and educational institutions, the images of many valuable objects like IDcards, photographs, credit cards, and transaction receipts are stored andtransmitted to the digital environment. These images are very significantand must be secured. A valuable image can be maliciously modified by anattacker. The modification of an image is sometimes imperceptible even by theperson who stored the image. In this paper, an active image forgery detectionmethod that encodes and decodes image edge information is proposed. Theproposed method is implemented by designing an interface and applied on atest image which is frequently used in the literature. Various tampering attacksare simulated to test the fidelity of the method. The method not only notifieswhether the image is forged or not but also marks the tampered region ofthe image. Also, the proposed method successfully detected tampered regionsafter geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEGcompression.

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrast-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical techniques.With this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved regions.To perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful vectors.Finally,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.

Deep Learning-Based Digital Image Forgery Detection Using Transfer Learning

Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified.In the current cyber world where deepfakes have shaken the global community,confirming the legitimacy of a digital image is of great importance.With the advancements made in deep learning techniques,now we can efficiently train and develop state-of-the-art digital image forensic models.The most traditional and widely used method by researchers is convolution neural networks(CNN)for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.Therefore,in this study,a transfer learning based deep learning technique for image forgery detection is proposed.The proposed methodology consists of three modules namely;preprocessing module,convolutional module,and the classification module.By using our proposed technique,the training time is drastically reduced by utilizing the pre-trained weights.The performance of the proposed technique is evaluated by using benchmark datasets,i.e.,BOW and BOSSBase that detect five forensic types which include JPEG compression,contrast enhancement(CE),median filtering(MF),additive Gaussian noise,and resampling.We evaluated the performance of our proposed technique by conducting various experiments and case scenarios and achieved an accuracy of 99.92%.The results show the superiority of the proposed system.

OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Image Splicing Detection Using Generalized Whittaker Function Descriptor

Image forgery is a crucial part of the transmission of misinformation,which may be illegal in some jurisdictions.The powerful image editing software has made it nearly impossible to detect altered images with the naked eye.Images must be protected against attempts to manipulate them.Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications.Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images.Because image tampering detection targets processing techniques such as object removal or addition,identifying altered images remains a major challenge in research.In this study,a novel image texture feature extraction model based on the generalized k-symbolWhittaker function(GKSWF)is proposed for better image forgery detection.The proposed method is divided into two stages.The first stage involves feature extraction using the proposed GKSWF model,followed by classification using the“support vector machine”(SVM)to distinguish between authentic and manipulated images.Each extracted feature from an input image is saved in the features database for use in image splicing detection.The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel.When tested on publicly available image dataset“CASIA”v2.0(ChineseAcademy of Sciences,Institute of Automation),the proposed model had a 98.60%accuracy rate on the YCbCr(luminance(Y),chroma blue(Cb)and chroma red(Cr))color spaces in image block size of 8×8 pixels.The proposed image authentication model shows great accuracy with a relatively modest dimension feature size,supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.

Efficient Forgery Detection Approaches for Digital Color Images

This paper is concerned with a vital topic in image processing:color image forgery detection. The development of computing capabilitieshas led to a breakthrough in hacking and forgery attacks on signal, image,and data communicated over networks. Hence, there is an urgent need fordeveloping efficient image forgery detection algorithms. Two main types offorgery are considered in this paper: splicing and copy-move. Splicing isperformed by inserting a part of an image into another image. On the otherhand, copy-move forgery is performed by copying a part of the image intoanother position in the same image. The proposed approach for splicingdetection is based on the assumption that illumination between the originaland tampered images is different. To detect the difference between the originaland tampered images, the homomorphic transform separates the illuminationcomponent from the reflectance component. The illumination histogramderivative is used for detecting the difference in illumination, and henceforgery detection is accomplished. Prior to performing the forgery detectionprocess, some pre-processing techniques, including histogram equalization,histogram matching, high-pass filtering, homomorphic enhancement, andsingle image super-resolution, are introduced to reinforce the details andchanges between the original and embedded sections. The proposed approachfor copy-move forgery detection is performed with the Speeded Up RobustFeatures (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matchingwith Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogramequalization and single-mage super-resolution. Simulation results proved thefeasibility and the robustness of the pre-processing step in homomorphicdetection and SURF detection algorithms for splicing and copy-move forgerydetection, respectively.

Detecting JPEG image forgery based on double compression

Detecting the forgery parts from a double compressed image is very important and urgent work for blind authentication. A very simple and efficient method for accomplishing the task is proposed. Firstly, the probabilistic model with periodic effects in double quantization is analyzed, and the probability of quantized DCT coefficients in each block is calculated over the entire iraage. Secondly, the posteriori probability of each block is computed according to Bayesian theory and the results mentioned in first part. Then the mean and variance of the posteriori probability are to be used for judging whether the target block is tampered. Finally, the mathematical morphology operations are performed to reduce the false alarm probability. Experimental results show that the method can exactly locate the doctored part, and through the experiment it is also found that for detecting the tampered regions, the higher the second compression quality is, the more exact the detection efficiency is.

基于改进显著图和局部特征匹配的copy-move窜改检测

检测整幅窜改图像的方法增加了许多非必要的计算量,为了降低计算复杂度和进一步提高检测精确率,提出了一种基于改进显著图和局部特征匹配的copy-move窜改检测方法。首先,结合图像梯度改进显著图,分离出包含图像高纹理信息的局部显著区域;其次,只对该局部区域采用SIFT(scale invariant feature transform)算法提取特征点;然后,对显著性小的图像采用密度聚类和二阶段匹配策略,对显著性大的图像采用超像素分割和显著块特征匹配的策略;最后,结合PSNR和形态学操作来定位窜改区域。在两个公开数据集上进行实验,该方法的平均检测时间小于10 s,平均检测精确率大于97%,均优于所对比的方法。实验结果表明,该方法能够大幅缩减检测时间、有效提高检测精确率,并且对几何变换和后处理操作也都具有较好的鲁棒性。

Exposing Image Forgery with Inconsistent Reflection Line Midpoint

In recent years,there has been a backlash of sorts and the authenticity of images has been routinely questioned.Seeing is no longer believing.There is an urgent need for robust image forensic techniques to expose photo forgery.This paper proposed a novel and effective technique to expose image forgery using inconsistent reflection.More specifically,a new technique was presented to calculate reflection line midpoint,the definition of midpoint ratio was given,and three standards were proposed and employed to detect image forgery.Accuracy and effectiveness of the proposed technique were evaluated using a data set consisting of 200 authentic and forged images.Experimental results indicate that the proposed method can detect image forgery with very high success rate.

Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier.With image processing software at one’s fingertips,one can easily alter the content of an image,and the altered image is so realistic that it is illegible to the naked eye.These tampered images have posed a serious threat to personal privacy,social order,and national security.Therefore,detecting and locating tampered areas in images has important practical significance,and has become an important research topic in the field of multimedia information security.In recent years,deep learning technology has been widely used in image tampering localization,and the achieved performance has significantly surpassed traditional tampering forensics methods.This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning.According to the two types of tampering detection based on deep learning,the detection tasks of the method are detailed separately,and the problems and future prospects in this field are discussed.It is quite different from the existing work:(1)This paper mainly focuses on the problem of image tampering detection,so it does not elaborate on various forensic methods.(2)This paper focuses on the detectionmethod of image tampering based on deep learning.(3)This paper is driven by the needs of tampering targets,so it pays more attention to sorting out methods for different tampering detection tasks.

Detection of Copy-Scale-Move Forgery in Digital Images Using SFOP and MROGH

Social network platforms such as Twitter, Instagram and Facebook are one of the fastest and most convenient means for sharing digital images. Digital images are generally accepted as credible news but, it may undergo some manipulations before being shared without leaving any obvious traces of tampering; due to existence of the powerful image editing softwares. Copy-move forgery technique is a very simple and common type of image forgery, where a part of the image is copied and then pasted in the same image to replicate or hide some parts from the image. In this paper, we proposed a copy-scale-move forgery detection method based on Scale Invariant Feature Operator (SFOP) detector. The keypoints are then described using MROGH descriptor. Experimental results show that the proposed method is able to locate and detect the forgery even if under some geometric transformations such as scaling.

基于不变矩的Copy-Move型篡改图像盲检测方法

拷贝粘贴(Copy-Move)是极为常见的图像篡改方式之一。为了快速有效地检测这种图像篡改,该文提出了一种基于不变矩的Copy-Move型篡改图像盲认证方法,实验结果表明,此方法不仅可以检测传统的Copy-Move型篡改,而且可以检测出经过旋转、镜像以及缩放的Copy-Move型篡改,同时,为降低算法的复杂度,还提出利用块迭代划分方法来有效减少搜索计算量。仿真实验结果表明,该方法是有效的。

基于FMT的快速Copy-Move篡改检测

为了对图像篡改中常用的复制-移动伪造进行检测,基于傅里叶-梅林变换的平移、旋转和缩放的不变性提出一种快速图像区域分割和匹配的高效篡改检测算法。不同于以往模板匹配方式中按照单像素点移动得到重叠块划分方法,该算法采用相邻图像块的图像区域分割方式来减少整个图像块的数量。通过相似性匹配检测,得到初步的复制图像区域,然后利用边缘处理的方法处理改善篡改区域,从而达到改进篡改检测算法的效率和准确性。最后通过实验验证了该算法的有效性。

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.