Image Tampering Research Articles

Sudoku-Based Watermarking Scheme for Image Tampering Localization and Self-Recovery

Sudoku-Based Watermarking Scheme for Image Tampering Localization and Self-Recovery

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. This makes 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.

Zero-bit fragile watermarking for medical image tamper detection and recovery using RS code and lifting wavelet transform

ABSTRACT Medical images are habitually composed of Regions of Interest (ROI) and Region of Non-interest (RONI). ROI means the area containing the crucial information for diagnosis. To achieve the fidelity and integrity of the ROI, we propose a novel watermarking approach based on two great concepts zero-watermarking and Error Correcting Code (ECC). Tamper detection and recovery information are stored in RONI which accepts some visual quality degradation. Experimental results prove that our proposed approach produces a good performance in terms of imperceptibility and the ability to resist several hard attacks. The proposed scheme can recover 100% of the ROI until 15% of distortion is caused by salt and pepper noise. For multiplicative and Gaussian noise, the scheme has also a good capability of recovery (between 70% and 100%). With close to 20% of distortion, the system can recover 50% of the ROI. The proposed method has also a good capability of recovery against cropping and image processing operations.

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

Design of Image Copy-Paste Forensics System Based on Moment Invariants

Copy-paste tampering is a common type of digital image tampering, which refers to copying a part of the image area in the same image, and then pasting it into another area of the image to generate a forged image, so as to carry out malicious operations such as fraud and framing. This kind of malicious forgery leads to the security problem of digital image. The research of digital image copy paste forensics has important theoretical significance and practical value. For digital image copy-paste tampering, this paper is based on moment invariant image copy paste tampering detection algorithm, and use Matlab software to design the corresponding tampering forensics system.

Verifiable image revision from chameleon hashes

In a digital society, the rapid development of computer science and the Internet has greatly facilitated image applications. However, one of the public network also brings risks to both image tampering and privacy exposure. Image authentication is the most important approaches to verify image integrity and authenticity. However, it has been challenging for image authentication to address both issues of tampering detection and privacy protection. One aspect, image authentication requires image contents not be changed to detect tampering. The other, privacy protection needs to remove sensitive information from images, and as a result, the contents should be changed. In this paper, we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection. Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose (for example, cover some sensitive areas with mosaics) according to specific rules and verify the authenticity without interaction with the original authenticator. The security of our scheme is guaranteed by the security of the underlying cryptographic primitives. Experiment results show that our scheme is efficient and practical. We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.

Efficient image tampering localization using semi-fragile watermarking and error control codes

In this paper, we propose an image tampering localization algorithm using semi-fragile watermarking and Error-Locating codes in the DWT domain. By introducing different families of codes, we show the benefit in terms of image tampering localization and complexity of using control code error localization as an authentication function. Indeed, we first experimentally show that error localization block codes is as precise as using classical error correcting codes (Reed-Solomon and BCH codes) to locate image tampering. However, their corresponding decoding algorithms complexity is at least quadratic which make them impractical for some real time applications. To solve this problem, we introduce error-control codes called Error-Locating codes where error localization is reduced to a single syndrome computation performed with low number of binary operations (detailed later in the paper). We provide comparisons of image quality and tampering localization performances using error-detection, error-localization and error-correction approaches with different error control codes.

Image tampering detection based on a statistical model

Image tampering detection based on a statistical model

Web and Social Media Image Forensics for News Professionals

User-generated content -commonly referred to as "eyewitness media"- has become an essential component in journalism and news reporting. Increasingly more news providers, such as news agencies, broadcasters and Web-only players have set up teams of dedicated investigators or are in the process of training parts of their journalistic workforce to gather and evaluate material from social networks and the Web. If verified, such content can be invaluable in delivering a news story. However, while source checking and verification is as old as journalism itself, the verification of digital material is a relatively young field, with protocols and assisting tools still being developed. In this work, we present our efforts towards a Web-based image verification platform. The platform, currently in its alpha stage, features image tampering detection using a number of state-of-the-art algorithms and image metadata visualization. We discuss the current strengths and limitations of the platform and the implemented state-of-the-art with respect to the specific requirements of the task, resulting from its Web-based nature and its intended use by news investigators with limited expertise in the domain of image forensics.

Multiscale Content-Independent Feature Fusion Network for Source Camera Identification

In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.

Taylor‐RNet: An approach for image forgery detection using Taylor‐adaptive rag‐bull rider‐based deep convolutional neural network

Due to the use of powerful computers and advanced software for photo editing, image manipulation in digital images simply degrades the trust in digital images. Image forensic analysis focuses on image authenticity and image content. To process forensic research, different methods are introduced, which effectively differentiate fake images from the original image. A technique named image splicing is commonly used for image tampering, and the tampered image may be used in photography contents, news reports, and so forth, which brings negative influences among the society. Thus, for detecting spliced images, this paper proposed an automatic forgery detection approach named Taylor-adaptive rag-bull rider (RR) optimization algorithm-based deep convolutional neural network (Taylor-RNet). At first, the face of a human is detected from the spliced image using the Viola Jones algorithm, and later, to estimate light coefficients, the three-dimensional (3D) shape of the face is determined by using a landmark-based 3D morphable model (L3DMM). Then, distance measures, like, Bhattacharya, Euclidean, Seuclidean, Chebyshev, correlation coefficients, and Hamming, are determined from the light coefficients that form the feature vector to the proposed Taylor-RNet, which identifies the spliced image. Taylor-adaptive RR is the integration of the Taylor series with the adaptive RR optimization algorithm. Finally, the experimental analysis is performed using four data sets, such as DSO-1, DSI-1, real data set, and hybrid data set. The analysis result of the proposed method obtained a maximum accuracy of 96.921%, true positive rate of 99.981%, and true negative rate of 99.783%.

Digital Image Recovery Based on Lifting Wavelet Transform

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack.

A fast technique to detect copy-move image forgery with reflection and non-affine transformation attacks

Copy-move forgery is one of the most frequently utilized image tampering technique which uses the segment of the same image to produce manipulated image by duplicating or concealing image regions. To remove suspicious traces of forgery, various attacks are applied over the tampered image which make forgery detection process too complicated. We propose a forgery detection technique in which Center Surround Extrema (CenSurE) detector is applied for keypoint detection from images. To compute keypoint descriptors, Local Image Permutation Interval Descriptor (LIPID) is used. Keypoint matching is performed using k-Nearest Neighbor (k-NN) technique with utilization of k-d tree and Best-Bin-First (BBF) search method. Grouping over keypoints is performed using Fuzzy C-Means (FCM) clustering. We apply Random Sample Consensus (RANSAC) algorithm to remove outliers obtained during forgery detection process. Experimental results show that proposed technique can effectively detect forged images containing reflection and non-affine transformation with geometrical attacks. In addition, proposed approach also shows robustness against erosion, dilation, RGB color addition, zoom motion blur, JPEG compression, spread noise addition, and multiple copy-move attacks. Proposed scheme consumes least time in forgery detection as compared to state-of-the-art methods.

Perceptual hash-based coarse-to-fine grained image tampering forensics method

As an active forensic technology, perceptual image hash has important application in image content authenticity detection and integrity authentication. In this paper, we propose a hybrid-feature-based perceptual image hash method that can be used for image tampering detection and tampering localization. In the proposed method, we use the color features of image as global features, use point-based features and block-based features as local features, and combine with the structural features to generate intermediate hash code. Then we encrypt and randomize to generate the final hash code. Using this hash code, we present a coarse-to-fine grained forensics method for image tampering detection. The proposed method can realize object-level tampering localization. Abundant experimental results show that the proposed method is sensitive to content changes caused by malicious attacks, and the tampering localization precision achieves pixel level, and it is robust to a wide range of geometric distortions and content-preserving manipulations. Compared with the state-of-the-art schemes, the proposed scheme yields superior performance.

Image Forgery Detection and Localization

A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.

Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a semi-fragile watermarking scheme for JPEG2000 image self-authentication that ensuring the robustness of the watermark against the compression attacks generated by the JPEG2000 encoder itself. The scheme is combined with the JPEG2000 encoder by embedding the generated watermark into the host image during the JPEG2000 compression process. To generate the watermark we suppose to use a perceptual hash function (PHF) operating on discrete wavelet coefficients of the host image. The proposed watermark generation process leads the system to verify the integrity of the image without the need to any file except for the watermarked image. The watermark is embedded during the JPEG2000 compression process after Discrete Wavelet Transform (DWT) step into the approximation sub-band coefficients of the five wavelet decomposition using Index Modulation Quantification (QIM), and can be extracted during image decoding. To prove the authentication of the image, the system compares the extracted watermark with the new watermark generated from the received image. Experimental results show that our proposed approach has not only an extremely high accuracy of tampering detection but also a relatively very high resistance against JPEG2000 compression attacks.

Detecting fake news by exploring the consistency of multimodal data

During the outbreak of the new Coronavirus (2019-nCoV) in 2020, the spread of fake news has caused serious social panic. Fake news often uses multimedia information such as text and image to mislead readers, spreading and expanding its influence. One of the most important problems in fake news detection based on multimodal data is to extract the general features as well as to fuse the intrinsic characteristics of the fake news, such as mismatch of image and text and image tampering. This paper proposes a Multimodal Consistency Neural Network (MCNN) that considers the consistency of multimodal data and captures the overall characteristics of social media information. Our method consists of five subnetworks: the text feature extraction module, the visual semantic feature extraction module, the visual tampering feature extraction module, the similarity measurement module, and the multimodal fusion module. The text feature extraction module and the visual semantic feature extraction module are responsible for extracting the semantic features of text and vision and mapping them to the same space for a common representation of cross-modal features. The visual tampering feature extraction module is responsible for extracting visual physical and tamper features. The similarity measurement module can directly measure the similarity of multimodal data for the problem of mismatching of image and text. We assess the constructed method in terms of four datasets commonly used for fake news detection. The accuracy of the detection is improved clearly compared to the best available methods.

General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)

Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.

Identifying tampering operations in image operator chains based on decision fusion

There has been great interest in image forensics in recent years. However, most of the existing research focuses on detecting a certain tampering operation, which means that the introduced features usually depend on the investigated operation and only binary classification is considered. Given the case where the image tampering process involves diverse processing operations, we propose a decision fusion method for identifying tampering operations in operator chains in this work. The proposed method permits the integration of knowledge provided by available image forensic algorithms. Under this method, a similarity coefficient function is introduced to assign the weight of the output of each forensic classifier. Then, we utilize a combination rule based on local conflict management to merge these outputs. Comparison with the previous works shows an improvement in operations identification accuracy when an image has experienced multiple falsifications.

Comparative analysis of feature extraction and fusion for blind authentication of digital images using chroma channels

Blind authentication is one of the challenging techniques which has attracted considerable attention with the increasing security hacks on digital images. This paper evaluates different models based on feature extraction to detect digitally-altered images. Various feature-extraction methods have been investigated and compared including LBP, MSLBP, CSLBP, SLBP, WLD, LPQ, MSLPQ and DCT. Moreover, we propose a number of ways to combine a variety of features utilizing single and multi-scale representations of images. For classification, SVM is employed with different kernels to identify forged and authentic images. To evaluate the effectiveness of the investigated models, several experiments have been conducted using k-fold cross-validation and computed performance measures for two benchmark image tampering datasets (CASIA v1.0 and CASIA v2.0). Additionally, we have conducted statistical analysis for the top-ten models and the results confirmed that the best models for CASIA v1.0 and CASIA v2.0 are MSLBP-DCT and MSLPQ, respectively. Further improvements have been achieved by integrating features from the three color channels (Y, Cb and Cr) with and without feature reduction using PCA and LLP. In this case, the results show that MSLPQ-DCT achieved a better accuracy of 98.56% on CASIA v1.0 with 1020 features and MSLPQ achieved a slightly better accuracy of 97.4% on CASIA v2.0 with 1536 features.