Multi-Purpose Forensics of Image Manipulations Using Residual-Based Feature

The multi-purpose forensics is an important tool for forge image detection.In this paper,we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations,including spatial low-pass Gaussian blurring,median filtering,re-sampling,and JPEG compression.To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature,a residual group which contains several high-pass filtered residuals is introduced.The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way.Besides that,we also combine autoregressive coefficient and transition probability to form the proposed composite feature which is used to measure how manipulations change the neighborhood relationships in both linear and non-linear way.After a series of dimension reductions,the proposed feature set can accelerate the training and testing for the multi-purpose forensics.The proposed feature set is then fed into a multi-classifier to train a multi-purpose detector.Experimental results show that the proposed detector can identify several typical image manipulations,and is superior to the complicated deep CNN-based methods in terms of detection accuracy and time efficiency for JPEG compressed image with low resolution.

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.

Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.

Image Tampering Detection Using No-Reference Image Quality Metrics

In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

Digital Forensics for Recoloring via Convolutional Neural Network

As a common medium in our daily life,images are important for most people to gather information.There are also people who edit or even tamper images to deliberately deliver false information under different purposes.Thus,in digital forensics,it is necessary to understand the manipulating history of images.That requires to verify all possible manipulations applied to images.Among all the image editing manipulations,recoloring is widely used to adjust or repaint the colors in images.The color information is an important visual information that image can deliver.Thus,it is necessary to guarantee the correctness of color in digital forensics.On the other hand,many image retouching or editing applications or software are equipped with recoloring function.This enables ordinary people without expertise of image processing to apply recoloring for images.Hence,in order to secure the color information of images,in this paper,a recoloring detection method is proposed.The method is based on convolutional neural network which is quite popular in recent years.Unlike the traditional linear classifier,the proposed method can be employed for binary classification as well as multiple labels classification.The classification performance of different structure for the proposed architecture is also investigated in this paper.

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.

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.

Deep Learning for Distinguishing Computer Generated Images and Natural Images:A Survey

With the development of computer graphics,realistic computer graphics(CG)have become more and more common in our field of vision.This rendered image is invisible to the naked eye.How to effectively identify CG and natural images(NI)has been become a new issue in the field of digital forensics.In recent years,a series of deep learning network frameworks have shown great advantages in the field of images,which provides a good choice for us to solve this problem.This paper aims to track the latest developments and applications of deep learning in the field of CG and NI forensics in a timely manner.Firstly,it introduces the background of deep learning and the knowledge of convolutional neural networks.The purpose is to understand the basic model structure of deep learning applications in the image field,and then outlines the mainstream framework;secondly,it briefly introduces the application of deep learning in CG and NI forensics,and finally points out the problems of deep learning in this field and the prospects for the future.

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.

An Overview of Image Tamper Detection

With the popularization of high-performance electronic imaging equipment and the wide application of digital image editing software,the threshold of digital image editing becomes lower and lower.Thismakes it easy to trick the human visual system with professionally altered images.These tampered images have brought serious threats to many fields,including personal privacy,news communication,judicial evidence collection,information security and so on.Therefore,the security and reliability of digital information has been increasingly concerned by the international community.In this paper,digital image tamper detection methods are classified according to the clues that they rely on,detection methods based on image content and detection methods based on double JPEG compression traces.This paper analyzes and discusses the important algorithms in several classification methods,and summarizes the problems existing in various methods.Finally,this paper predicts the future development trend of tamper detection.

Passive detection of copy-paste forgery between JPEG images

A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.

PHOTOREALISTIC COMPUTER GRAPHICS FORENSICS BASED ON LEADING DIGIT LAW

As the advent and growing popularity of image rendering software,photorealistic computer graphics are becoming more and more perceptually indistinguishable from photographic images.If the faked images are abused,it may lead to potential social,legal or private consequences.To this end,it is very necessary and also challenging to find effective methods to differentiate between them.In this paper,a novel leading digit law,also called Benford's law,based method to identify computer graphics is proposed.More specifically,statistics of the most significant digits are extracted from image's Discrete Cosine Transform(DCT) coefficients and magnitudes of image's gradient,and then the Support Vector Machine(SVM) based classifiers are built.Results of experiments on the image datasets indicate that the proposed method is comparable to prior works.Besides,it possesses low dimensional features and low computational complexity.

Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.

Investigating the Implications of Virtualization for Digital Forensics

Research in virtualization technology has gained significant developments in recent years, which brings not only opportunities to the forensic community, but challenges as well. This paper discusses the potential roles of virtualization in digital forensics, examines the recent progresses which use the virtualization techniques to support modem computer forensics. The influences on digital forensics caused by virtualization technology are identified. Tools and methods in common digital forensic practices are analyzed, and experiences of our practice and reflections in this field are shared.

Resampling Factor Estimation via Dual-Stream Convolutional Neural Network

The estimation of image resampling factors is an important problem in image forensics.Among all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest.However,because of inherent ambiguity,spectrum-based methods fail to discriminate upscale and downscale operations without any prior information.In general,the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image.Firstly,the resampling process will introduce correlations between neighboring pixels.In this case,a set of periodic pixels that are correlated to their neighbors can be found in a resampled image.Secondly,the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks.Hence,in this paper,we propose a dual-stream convolutional neural network for image resampling factors estimation.One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images.The other is frequency stream that discovers the differences of spectrum between rescaled and original images.The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain,which is later fed into softmax layer for resampling factor estimation.Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.

Forgery Detection Using Inconsistent Reflection Vanishing Point

The rapid development of image processing techniques has made it extremely easy to alter the content of images or create newimages.So photographs,which appear in magazines,social media,and political attacks,can no longer be trusted.A novel and effective technique is proposed in this paper to expose image forgery using inconsistent reflection vanishing point(RVP).More specifically,the definition of error distance is given,sin^2()-based function is proposed to normalize error distance,and a reasonable threshold value is set to detect image forgery.The experimental data and results are presented to demonstrate the accuracy and effectiveness of the technique.

Discriminating between photorealistic computer graphics and natural images using fractal geometry

Rendering technology in computer graphics （CG） is now capable of producing highly photorealistic images, giving rise to the problem of how to identify CG images from natural images. Some methods were proposed to solve this problem. In this paper, we give a novel method from a new point of view of image perception. Although the photorealistic CG images are very similar to natural images, they are surrealistic and smoother than natural images, thus leading to the difference in perception. A pert of features are derived from fractal dimension to capture the difference in color perception between CG images and natural images, and several generalized dimensions are used as the rest features to capture difference in coarseness. The effect of these features is verified by experiments. The average accuracy is over 91.2%.

Forensic Human Image Identification Using Medical Indicators

Diseases not only bring troubles to people’s body functions and mind but also influence the appearances and behaviours of human beings.Similarly,we can analyse the diseases from people’s appearances and behaviours and use the personal medical history for human identification.In this article,medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced,and were applied in the field of forensic identification of human images,which we called medical forensic identification of human images(mFIHI).The proposed method analysed the people’s medical signs by studying the appearance and behaviour characteristics depicted in images or videos,and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects.Through a conformity and difference analysis on medical indicators and their indicated diseases,it would provide an important information for human identification from images or videos.A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI,and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.

Detecting Shifted Double JPEG Compression Tampering Utilizing Both Intra-Block and Inter-Block Correlations

Copy-paste forgery is a very common type of forgery in JPEG images.The tampered patch has always suffered from JPEG compression twice with inconsistent block segmentation.This phenomenon in JPEG image forgeries is called the shifted double JPEG(SDJPEG) compression.Detection of SDJPEG compressed image patches can make crucial contribution to detect and locate the tampered region.However,the existing SDJPEG compression tampering detection methods cannot achieve satisfactory results especially when the tampered region is small.In this paper,an effective SDJPEG compression tampering detection method utilizing both intra-block and inter-block correlations is proposed.Statistical artifacts are left by the SDJPEG compression among the magnitudes of JPEG quantized discrete cosine transform(DCT) coefficients.Firstly,difference 2D arrays,which describe the differences between the magnitudes of neighboring JPEG quantized DCT coefficients on the intrablock and inter-block,are used to enhance the SDJPEG compression artifacts.Then,the thresholding technique is used to deal with these difference 2D arrays for reducing computational cost.After that,co-occurrence matrix is used to model these difference 2D arrays so as to take advantage of second-order statistics.All elements of these co-occurrence matrices are served as features for SDJPEG compression tampering detection.Finally,support vector machine(SVM) classifier is employed to distinguish the SDJPEG compressed image patches from the single JPEG compressed image patches using the developed feature set.Experimental results demonstrate the efficiency of the proposed method.