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The demonstration of (a) Bayer’s filter mosaic; (b) the mosaic of acquired green channel ‘A’ and interpolated green channel ‘I’
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The image acquisition device, the light is filtered through a Color Filter Array (CFA), where each pixel captures only one color (from Red, Green, and Blue), while others are calibrated. This process is known as interpolation process, and the artifacts introduced are called CFA or interpolation artifacts. The structure of these artifacts in the ima...
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Citations
... Ferrara et al. [27] proposed a feature based on the prediction error variance to measure the absence and presence of CFA traces to obtain a fine-grained tampering possibility map that can detect small forgery. Singh et al. [28] introduced Markov random process to reduce the false detections and computational complexity on the basic study of Ferrara et al. [27]. Lu et al. [29] applied broad first search neighbors clustering algorithm to detect copied regions and duplicated regions in the copy-move images. ...
The color filter array of the camera is an effective fingerprint for digital forensics. Most previous color filter array (CFA)-based forgery localization methods perform under the assumption that the interpolation algorithm is linear. However, interpolation algorithms commonly used in digital cameras are nonlinear, and their coefficients vary with content to enhance edge information. To avoid the impact of this impractical assumption, a CFA-based forgery localization method independent of linear assumption is proposed. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90.
... Splicing objects as of one image to another, removing of regions or objects as of images, generating object's copies within the same image, along with more are numerous image forgeries [5,6]. Researchers have propounded methods centered on numerous techniques, namely (A) JPEG compression artifacts, (B) resampling detection, (C) lighting artifacts, (D) noise inconsistencies, (E) camera sensor noise, etc. for detecting these forgeries [7,8]. But, a particular sort of manipulation or a set of similar tamper operations is addressed by most techniques. ...
In different scientific and security or surveillance applications, the identification of content-altering manipulation from a video or an image has emerged as a field of increasing interest. Enormous traditional methodologies have been developed over time for detecting image forgeries. Nevertheless, most image forgery methodologies, which exist in the literature are constrained to detecting a specific sort of forgery (either image splicing or else copy-move). Thus, a scheme capable of efficiently as well as accurately detecting the availability of unseen forgeries in an image is essential. A Logit Normalization and Distance Intersection over Union-centered fast regional convolutional neural network (LogDIoU-Faster RCNN) technique is created by the work aimed at IF along with region detection. Concurrently, the region localization and also forgery detection process is executed. Utilizing Dual Band Pass Filter Based Steerable Pyramid Transform (DBPF-SPT), the image is initially decomposed by the framework into numerous sub-bands. For recognizing the forgery along the disparate scales, frequencies, along with orientations, decomposition is used. After that, utilizing Normalized Extended Local Ternary Pattern-Principal Component Analysis (NELTP-PCA), the essential key points are extracted together with chosen from the sub-bands that preserve the image’s informative features and also reduce computational complexity. The image is finally identified by LogDIoU- Faster RCNN as a forgery image or not, and also the forgery’s location is localized. Superior detection accuracy concerning both categorizing the forgery and also identifying the region is attained by the framework. It stays robust when analogized to the existent top-notch methods as exhibited by the experimental outcomes.
... Fig. 2 An example of forgery in digital image editing process. These traces are in the form of added heterogeneous noise [32,33,37,49] by sensors, light intensity variation due to lens aberration [8,40,46], an inconsistent pattern of color filter array [10,42], artifacts during JPEG compression [17,26,27] and many more. These footprints are quite effective for the detection of forged regions in an image but have several shortcomings and challenges. ...
... The absence of periodic interpolation structures identifies forged location in fake images [10,42,43]. A very fundamental limitation of this type of detection is the periodic structure can be destroyed by compressing the image. ...
A forged image is a major source of counterfeit news and is mostly used in a spiteful manner such as exciting targeted mob, identity theft, defaming individual, or misleading law bodies. Therefore, a technique is required to detect the tampered regions in a forged image. Deep learning is surpassing technology for prediction or classification tasks in images. Challenges in this technology are a variety of datasets to train the model and specific architecture for a specific application. In this paper, a deep learning model is extended for the localization of tampered regions in a forged image. This is an extension of the well-known U-Net segmentation model. In the proposed model, batch normalization layers and identity-blocks are placed at suitable places of the U-Net model to overcome the challenges such as overfitting and loss of information during max-pooling. To overcome the challenge of the dataset five different publicly available datasets are taken to train, validate and test the model. The trained model is also tested on four created forged images (not belong to the dataset) whose acquisition sources may different i.e. medical image, identity document, natural image, and scanned report. The result of the proposed model is compared with state-of-the-art techniques which show that the method works better than others.
... Markov process has already proved its robustness against photo forgery [6][28], steganalysis [29], median filtering detection [30], and many other applications. Due to this Markov process is utilized for feature extraction. ...
A detection of fake photos is a serious concern since general users can easily create with mobile apps and computer software. In this paper, a novel method that can detect fake photos accurately is proposed. RGB color model permutations are considered and non-decimated shift-invariant wavelet transform is applied. The proposed method extracts features using the Markov process and texture operator based on co-occurrence in both the spatial and frequency domains. The feature vector dimension is reduced by using an infinite feature selection algorithm and feature selection provides quality features to improve a detection accuracy and reduce a classification model training time. The experimental analysis is performed on four photo forgery datasets and demonstrated the accuracy of the proposed scheme is outstanding for both types of forgery, splicing and copy-move when compared with previous forgery detection schemes.
... A higher-order statistical approach for the detection of inconsistencies in the artifacts of different parts of the image is to expose any forgery was given by Singh and his team in 2018 [31]. Various features have been developed to detect whether Color Filter Array (CFA) artifacts are present or not in a region of the image using Markov Transition Probability Matrix (MTPM). ...
Digital imaging has become elementary in this novel era of technology with unconven-tional image forging techniques and tools. Since, we understand that digital image forgery is possible, it cannot be even presented as a piece of evidence anywhere. Dissecting this fact, we must dig unfathomable into the issue to help alleviate such derelictions. Copy-move and splicing of images to create a forged one prevail in this monarchy of digitalization. Copy-move involves copying one part of the image and pasting it to another part of the image while the latter involves merging of two images to significantly change the original image and create a new forged one. In this article, a novel slant using a convolutional neural network (CNN) has been proposed for automatic detection of copy-move forgery detection. For the experimental work, a benchmark dataset namely, MICC-F2000 is considered which consists of 2000 images in which 1300 are original and 700 are forged. The experimental results depict that the proposed model outperforms the other traditional methods for copy-move forgery detection. The results of copy-move forgery were highly promising with an accuracy of 97.52% which is 2.52% higher than the existing methods.
... Particularly, a single forensic detector is effective with a specific type of images. For example, the methods based on analysing device characteristics such as sensor pattern noise, Photo Response Non-Uniformity noise (PRNU) [6], [7], [8], color filter array (CFA) [9], [10] work well for RAW or TIFF images but worse for detecting JPEG forgery images with low quality compression. The algorithms based on exploiting the double quantization artifacts hidden among the DCT coefficients in forgery JPEG images to localize the tampered regions [11], [12], [13] fail in detecting forgery images which is processed in RAW and resave in JPEG. ...
With the advent of digital imaging, it has become fairly easy to modify the content of an image in many different ways while leaving no obvious visual clue. This has further challenged many existing image forensic techniques. The techniques which perform well with one specific kind of forgeries still suffer from strong limitations when dealing with realistic tampered images. Therefore, an effective strategy for tampering detection and localization requires the application of fusion technique. Although there have been extensive researches on fusion technique on different fields, there has never been a systematic study about fusion technique in image forensic domain. In this paper, we provide a thorough review on the state-of-the-art of fusion methods applied in tampering image detection and localization domain. We then present a practical comparison of two popular fusion techniques: Bayesian and Dempster-Shafer theory (DST) based fusion. The comparison relies on two applications which leverage the two aforementioned fusion techniques. In the first case, aggregating the decision maps of two forensic approaches: Photo Response Non Uniformity (PRNU) and statistical features based approaches has improved the forgery detection performance on saturated and dark regions of images. In the second case, integrating the decision maps of the forensic approach using demosaicing artifacts and the forensic approach using SIFT descriptors and local color dissimilarity maps has enhanced the detection performance on both copy-moved and copypasted forgeries images. Experiments show that the DST based fusion performs better in the first case while the Markov Random Field (MRF) based fusion performs better in the second case. It can be concluded that each technique has its own advantages and the best choice depends on each situation and users’ requirements.
... Today, every person is capable to edit the video and to post it on the social media which can raise controversial issues in the real world scenario [20]. The investigators have trusted that most of controversial issues are raised by the digital videos which are posted on social media by individual users [48]. The computer graphics rendering software is also used to generate images which are more difficult to differentiate from its original images. ...
... Now, these realistic cases in the digital videos can also be seen in the social media sites which are forged with the chroma key foreground forgery. The footprints leaving behind on frame after manipulating the digital video can be removed by implementing various actions [48] so that nobody can detect this manipulation in the forged digital video [46]. These various kinds of actions are known as attacks. ...
Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques.
... 2. In most demosaicing traces-based algorithms (see e.g., [63,134,174,194]), prediction residues are given from entire pixels. Here, by further partitioning a pixel into content and noise parts, we realize that the demosaicing behaves in the same manner for content, noise, as well as entire pixel. ...
... . Dans la plupart des algorithmes de dématriçage basés sur les traces (voir[63,134,174,194]), les résidus de prédiction sont donnés à partir des valerus des pixels. Ici, en partitionnant un pixel en 2 parties comportant le contenu et le bruit, nous réalisons que le dématriçage se comporte de la même manière pour le contenu et le bruit. ...
In today’s digital age, the trustworthiness of image content is of great concern due to the dissemination of easy-to-use and low-cost image editing tools. Forged images can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Faced with such a serious situation, we develop in this doctoral project three versatile techniques based on (i) demosaicing traces (ii) JPEG compression traces, and (iii) resampling traces for detecting forged digital images and localizing various types of tampering therein. Although these techniques are different, they work under the common assumption that manipulations may alter some underlying statistical properties of natural images. A two-steps detection process has been adopted for every detection technique: (i) analyze and model statistical features of both the authentic and forged images associated with specific in-camera and/or post-camera traces, then (ii) design a statistical detector to differentiate between the authentic and forged images by estimating statistical changes in their models. Various numerical experiments on several well-known benchmark datasets highlight the performances and robustness of the proposed detection techniques.
... Some approaches [40,41,42,43] assumed that the estimated CRF must be unique for every sub portion of an image and try to detect if there exist a significantly different estimated CRF to expose traces of forgeries. Many methods [44,45,46,47,48,49] try to infer the CFA grid and try to expose misaligned or mismatch area as traces of forgeries. Other uses multiple photos from one device to estimates the PRNU [50,51,52,53,54,55] which allow them to authenticate later images from the same device or to detect forgeries. ...
Digital media are parts of our day-to-day lives. With years of photojournalism, we have been used to consider them as an objective testimony of the truth. But images and video retouching software are becoming increasingly more powerful and easy to use and allow counterfeiters to produce highly realistic image forgery. Consequently, digital media authenticity should not be taken for granted any more. Recent Anti-Money Laundering (AML) relegation introduced the notion of Know Your Customer (KYC) which enforced financial institutions to verify their customer identity. Many institutions prefer to perform this verification remotely relying on a Remote Identity Verification (RIV) system. Such a system relies heavily on both digital images and videos. The authentication of those media is then essential. This thesis focuses on the authentication of images and videos in the context of a RIV system. After formally defining a RIV system, we studied the various attacks that a counterfeiter may perform against it. We attempt to understand the challenges of each of those threats to propose relevant solutions. Our approaches are based on both image processing methods and statistical tests. We also proposed new datasets to encourage research on challenges that are not yet well studied.
... When an image is tampered, forged regions exhibit demosaicing inconsistencies within authentic image regions. Accordingly, a number of studies have been conducted [35][36][37][38][39][40] to localize forged regions using demosaicing traces. However, the interpolation kernel for demosaicing is generally unknown. ...
... The performance of tampering localization can depend on the selection of the reinterpolation kernel. In general, the re-interpolation kernel is assumed to be bilinear, bicubic, or median [35][36][37]40]. These interpolation kernel types only use intra-channel information, and they are, therefore, inappropriate for demosaicing methods using inter-channel color information. ...
... Fig. 1 shows the typical process of forgery localization methods using CFA artifacts. First, the green channel [36,37,39] or all color channels [35,38,40] are selected to estimate the re-interpolation kernel. Next, prediction residue is generated using the difference between the tampered and reinterpolated images by the estimated kernel. ...
Almost all image sensors measure only one color per pixel through the color filter array. Missing pixels are estimated using a demosaicing process. For this reason, a demosaiced image leaves a particular trace. When an image is manipulated or tampered, the demosaicing trace can be changed. This change can serve as a basic clue for detecting or localizing image tampering. Demosaicing pattern-based tampering localization algorithms require a re-interpolation process, and the prediction residue between the given image and the re-interpolated image is commonly used to localize tampered regions. However, the prediction residue is not always valid because the demosaicing interpolation kernel cannot be known, which deteriorates the localization performance. This paper presents an effective re-interpolation process using singular value decomposition for an unknown demosaicing method. First, the green channel of the given image is decomposed into four sub-images according to the Bayer pattern. For a small block of each sub-image, the singular value decomposition is performed. The prediction residue is obtained by reconstructing the image block after removing the largest singular value. The feature to localize the forged regions is extracted by the logarithm ratio of the prediction residue variance. The proposed method does not require any statistical model for the extracted feature, because the prediction residue is more accurate than that of conventional methods. We perform intensive experiments for three test datasets and compare the proposed method with state-of-the-art tampering localization methods, the results of which indicate that the proposed scheme outperforms existing approaches.
