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A Thorough Investigation on image Forgery Detection

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 1489-1528页

作者： Anjani Kumar Rai Subodh Srivastava Department of Electronics&Communication Engineering NITPatnaIndia

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

关键词： Forgery detection digital forgery image forgery localization image segmentation image forensics multimedia security

Consistency features and fuzzy-based segmentation for shadow and reflection detection in digital image forgery

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Science China(Information Sciences) 2017年第8期60卷 83-100页

作者： Rajan CRISTIN Velankanni CYRIL RAJ St.Peter’s Institute of Higher Education and Research Dr.M.G.R Educational and Research Institute University

Advances in photo editing software have made it possible to generate visually convincing photographic forgeries which have been increased tremendously in recent years. In order to alleviate the problem of image forgery, a handful of techniques have been presented in the literature to detect forgery either in shadow or reflection. This paper aims to develop a technique to detect the image forgery either in shadow or reflection using features enabled neural network. The proposed technique of image forgery detection contains three important steps, like segmentation, feature extraction and detection. In segmentation, shadow points and reflection points are identified using map-based segmentation and FCM clustering. Then, feature points from the shadow points and reflective parts are extracted by considering texture consistency and strength consistency using LVP operator. The final step of forgery detection is performed using the feed forward neural network, where a new algorithm called ABCLM is developed for training of neural network weights. The performance is analyzed with four existing algorithms using measures such as accuracy and MSE. From the analysis, we understand that the proposed technique obtained the maximum accuracy of 80.49%.

关键词： image forensics forgery detection shadow reflection texture neural network

image composite authentication using a single shadow observation

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Science China(Information Sciences) 2015年第9期58卷 122-134页

作者： CAO XiaoChun ZHAO HanDong WANG Chuan ZHANG Wei State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences

The paper presents a novel method for image composite authentication based on a single shadow in an outdoor image. Existing methods based on the shadow geometry consistency involve more than two shadow observations. We relax the requirement of two shadow observations in the same scene. As a trade-off,we use the information in EXIF(Exchangeable image File) based on which the ground truth sun elevation is calculated. We also estimate the sun elevation based on geometric constraints the scene provides, whose consistency serves as the cue to authenticate image forgery. It is the first attempt, to our best knowledge, to make use of sun elevation with shadow of one object to authenticate outdoor images. Also, the novel use of GPS(Global Positioning System), and Date and Time data of captured image makes image forensics possible even in one image containing a single shadow. Experimental results on both the synthetic data and visually plausible images demonstrate the performance of the proposed method.

关键词： image forensics solar shadow sun elevation exchangeable image file geometry-based technique

Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector

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Chinese Journal of Electronics 2017年第2期26卷 345-350页

作者： LYU Yingda SHEN Xuanjing CHEN Haipeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

To detect copy-paste tampering,an improved SIFT(Scale invariant feature transform)-based algorithm was *** angle is defined and a maximum angle-based marked graph is *** marked graph feature vector is provided to each SIFT key point via discrete polar coordinate *** points are matched to detect the copy-paste tampering *** experimental results show that the proposed algorithm can effectively identify and detect the rotated or scaled copy-paste regions,and in comparison with the methods reported previously,it is resistant to postprocessing,such as blurring,Gaussian white noise and JPEG *** proposed algorithm performs better than the existing algorithm to dealing with scaling transformation.

关键词： Copy-paste tampering image forensics Marked graph Maximum angle

Exposing photo manipulation with inconsistent perspective geometry

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The Journal of China Universities of Posts and Telecommunications 2014年第4期21卷 83-91,104页

作者： LI Yan ZHOU Ya-jian YUAN Kai-guo GUO Yu-cui NIU Xin-xin Information Security Center Beijing University of Posts and Telecommunications

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value （NMV） and normalized standard deviation （NSD） of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.

关键词： digital images forgery detection image forensics vanishing points

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Digital forensics for Recoloring via Convolutional Neural Network

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Computers, Materials & Continua 2020年第1期62卷 1-16页

作者： Zhangyi Shen Feng Ding Yunqing Shi Electrical and Computer Engineering Department New Jersey Institute of Technology323 Martin Luther King BLVDNewark NJ 07102USA Computer Science Department State University of New York Albany1400 Washington AveAlbany NY 12222USA

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

关键词： image forensics machine learning convolutional neural network recoloring

Resampling Factor Estimation via Dual-Stream Convolutional Neural Network

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Computers, Materials & Continua 2021年第1期66卷 647-657页

作者： Shangjun Luo Junwei Luo Wei Lu Yanmei Fang Jinhua Zeng Shaopei Shi Yue Zhang School of Data and Computer Science Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhou510006China Academy of Forensic Science Shanghai200063China College of Information Science and Technology Jinan UniversityGuangzhou510632China Department of Computer Science University of Massachusetts LowellLowellMA 01854USA

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

关键词： image forensics image resampling detection parameter estimation convolutional neural network

image Tampering Detection Using No-Reference image Quality Metrics

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Journal of Harbin Institute of Technology(New Series) 2014年第6期21卷 51-56页

作者： Ying Li Bo Wang Xiang-Wei Kong Yan-Qing Guo School of Information and Communication Engineering Dalian University of Technology

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 *** 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 *** principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered *** particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered *** to four types of effective no-reference image quality metrics,we obtain 13 features to present an *** experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

关键词： image forensics tampering detection no-reference image quality metrics tampering localization

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Discriminating between photorealistic computer graphics and natural images using fractal geometry

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Science in China(Series F) 2009年第2期52卷 329-337页

作者： PAN Feng CHEN JiongBin HUANG JiWu Guangdong Key Lab of Information Security Technology School of Information Science and Technology Sun Yat-Sen UniversityGuangzhou 510275 China

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%.

关键词： image forensics computer graphics natural image fractal image authentication

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