Copy move Forgery Detection Approaches: A Survey

Copy-move forgery detection is one of the most popular image forgery technique in which a part of a digital image is copied and pasted to another part in the same image with the intension to make an object “disappear†from the image by covering it with a small block copied from another part of the same image. Hence, the main task of copy-move forgery detection is to detect image areas that are same or almost similar within an image. These method in general use two approaches namely key-point based and block based. This paper provides a review of copy move forgery detection on various techniques.Keywords: Copy move forgery, Lexicographical Sorting, Digital Image Forgery, Duplicated Region

Tampering detection and localization in images from social networks : A CBIR approach

International audienceStep 1 : Content-based image retrieval system • Goal : Find the best image in our database for a comparaison with the query • Method : 1. Find the 10 most similar images with a dot product using the de-scriptors explain below. Research is accelerated with a KD-Tree approach 2. Reorder these 10 candidates in order to find the best candidate. Define an homography between the image query and each candidate in order to found the best homography Descriptors used based on VGG19 [1] • Two sizes used : training images size or based on a kernelization step like [2] • Three vectors are analyzed based on the output of three layers: last convolutional layer C 5 with a output lenght of 512 and the fully-connected layers C 6 and C 7 with a output lenght of 4096 for both • A mean or max pooling have to be apply in the case of C 5 when we used the standard training images size and the three outputs in case of kernelized approach

Detection of Geometric Transformations in Copy-Move Forgery of Digital Images

Digital Forensics is a branch of forensic science which is related to cyber crime. It basically involves the detection, recovery and investigation of material found in digital devices. Digital images and videos plays most important role in digital forensics. They are the prime evidences of any crime scene. So the fidelity of the image is important. Digital images can be easily manipulated and edited with the help of image processing tools. Copy-move Forgery is the most primitive form of cyber attack on digital images. In Copy-move forgery a part of image (region) itself is copied and pasted into another part of the same image. The intension behind this type of attack is to “add” or “disappear” some objects from the image. Hence to break the fidelity of the image and fool the viewer. Copy-move attack is more prevalent in images having uniform texture or patterns, for e.g. sand, grass, water etc. In this thesis exact block matching is used as a detection technique. This technique is based on block matching, for these the whole image is divided into number of block and then the matching process is applied. Sometimes the copied region is processed before pasted i.e. some geometric transformations is applied on the pasted region. The transformations like scaling, rotation etc. It is not possible for human eyes to detect such kind of forgeries. Whenever forgery is done in this manner the common techniques like block matching, exhaustive search, auto-correlation and robust match etc. are not able to detect the forgery having geometric transformations. So that for identification of forged region we need some technique which are based on local features and also invariant to transformations. In this thesis SIFT is used for forgery detection. SIFT stands for Scale Invariant Feature Transform, this gives local feature points which are invariant to scales. The key points helps to find the duplicated region with different matching algorithm

Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

Nowadays, image forgery has become common because only an editing package software and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applications and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group compression. This study proposes an algorithm that detects image falsification on the basis of Hessian features

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

A Survey of Partition-Based Techniques for Copy-Move Forgery Detection

A copy-move forged image results from a specific type of image tampering procedure carried out by copying a part of an image and pasting it on one or more parts of the same image generally to maliciously hide unwanted objects/regions or clone an object. Therefore, detecting such forgeries mainly consists in devising ways of exposing identical or relatively similar areas in images. This survey attempts to cover existing partition-based copy-move forgery detection techniques

An ensemble architecture for forgery detection and localization in digital images

Questa tesi presenta un approccio d'insieme unificato - "ensemble" - per il rilevamento e la localizzazione di contraffazioni in immagini digitali. Il focus della ricerca è su due delle più comuni ma efficaci tecniche di contraffazione: "copy-move" e "splicing". L'architettura proposta combina una serie di metodi di rilevamento e localizzazione di manipolazioni per ottenere prestazioni migliori rispetto a metodi utilizzati in modalità "standalone". I principali contributi di questo lavoro sono elencati di seguito. In primo luogo, nel Capitolo 1 e 2 viene presentata un'ampia rassegna dell'attuale stato dell'arte nel rilevamento di manipolazioni ("forgery"), con particolare attenzione agli approcci basati sul deep learning. Un'importante intuizione che ne deriva è la seguente: questi approcci, sebbene promettenti, non possono essere facilmente confrontati in termini di performance perché tipicamente vengono valutati su dataset personalizzati a causa della mancanza di dati annotati con precisione. Inoltre, spesso questi dati non sono resi disponibili pubblicamente. Abbiamo poi progettato un algoritmo di rilevamento di manipolazioni copy-move basato su "keypoint", descritto nel capitolo 3. Rispetto a esistenti approcci simili, abbiamo aggiunto una fase di clustering basato su densità spaziale per filtrare le corrispondenze rumorose dei keypoint. I risultati hanno dimostrato che questo metodo funziona bene su due dataset di riferimento e supera uno dei metodi più citati in letteratura. Nel Capitolo 4 viene proposta una nuova architettura per predire la direzione della luce 3D in una data immagine. Questo approccio sfrutta l'idea di combinare un metodo "data-driven" con un modello di illuminazione fisica, consentendo così di ottenere prestazioni migliori. Al fine di sopperire al problema della scarsità di dati per l'addestramento di architetture di deep learning altamente parametrizzate, in particolare per il compito di scomposizione intrinseca delle immagini, abbiamo sviluppato due algoritmi di generazione dei dati. Questi sono stati utilizzati per produrre due dataset - uno sintetico e uno di immagini reali - con lo scopo di addestrare e valutare il nostro approccio. Il modello di stima della direzione della luce proposto è stato sfruttato in un nuovo approccio di rilevamento di manipolazioni di tipo splicing, discusso nel Capitolo 5, in cui le incoerenze nella direzione della luce tra le diverse regioni dell'immagine vengono utilizzate per evidenziare potenziali attacchi splicing. L'approccio ensemble proposto è descritto nell'ultimo capitolo. Questo include un modulo "FusionForgery" che combina gli output dei metodi "base" proposti in precedenza e assegna un'etichetta binaria (forged vs. original). Nel caso l'immagine sia identificata come contraffatta, il nostro metodo cerca anche di specializzare ulteriormente la decisione tra attacchi splicing o copy-move. In questo secondo caso, viene eseguito anche un tentativo di ricostruire le regioni "sorgente" utilizzate nell'attacco copy-move. Le prestazioni dell'approccio proposto sono state valutate addestrandolo e testandolo su un dataset sintetico, generato da noi, comprendente sia attacchi copy-move che di tipo splicing. L'approccio ensemble supera tutti i singoli metodi "base" in termini di prestazioni, dimostrando la validità della strategia proposta.This thesis presents a unified ensemble approach for forgery detection and localization in digital images. The focus of the research is on two of the most common but effective forgery techniques: copy-move and splicing. The ensemble architecture combines a set of forgery detection and localization methods in order to achieve improved performance with respect to standalone approaches. The main contributions of this work are listed in the following. First, an extensive review of the current state of the art in forgery detection, with a focus on deep learning-based approaches is presented in Chapter 1 and 2. An important insight that is derived is the following: these approaches, although promising, cannot be easily compared in terms of performance because they are typically evaluated on custom datasets due to the lack of precisely annotated data. Also, they are often not publicly available. We then designed a keypoint-based copy-move detection algorithm, which is described in Chapter 3. Compared to previous existing keypoints-based approaches, we added a density-based clustering step to filter out noisy keypoints matches. This method has been demonstrated to perform well on two benchmark datasets and outperforms one of the most cited state-of-the-art methods. In Chapter 4 a novel architecture is proposed to predict the 3D light direction of the light in a given image. This approach leverages the idea of combining, in a data-driven method, a physical illumination model that allows for improved regression performance. In order to fill in the gap of data scarcity for training highly-parameterized deep learning architectures, especially for the task of intrinsic image decomposition, we developed two data generation algorithms that were used to produce two datasets - one synthetic and one of real images - to train and evaluate our approach. The proposed light direction estimation model has then been employed to design a novel splicing detection approach, discussed in Chapter 5, in which light direction inconsistencies between different regions in the image are used to highlight potential splicing attacks. The proposed ensemble scheme for forgery detection is described in the last chapter. It includes a "FusionForgery" module that combines the outputs of the different previously proposed "base" methods and assigns a binary label (forged vs. pristine) to the input image. In the case of forgery prediction, our method also tries to further specialize the decision between splicing and copy-move attacks. If the image is predicted as copy-moved, an attempt to reconstruct the source regions used in the copy-move attack is also done. The performance of the proposed approach has been assessed by training and testing it on a synthetic dataset, generated by us, comprising both copy-move and splicing attacks. The ensemble approach outperforms all of the individual "base" methods, demonstrating the validity of the proposed strategy

Security of Forensic Techniques for Digital Images

Digital images are used everywhere in modern life and mostly replace traditional photographs. At the same time, due to the popularity of image editing tools, digital images can be altered, often leaving no obvious evidence. Thus, evaluating image authenticity is indispensable. Image forensic techniques are used to detect forgeries in digital images in the absence of embedded watermarks or signatures. Nevertheless, some legitimate or illegitimate image post-processing operations can affect the quality of the forensic results. Therefore, the reliability of forensic techniques needs to be investigated. The reliability is understood in this case as the robustness against image post-processing operations or the security against deliberated attacks. In this work, we first develop a general test framework, which is used to assess the effectiveness and security of image forensic techniques under common conditions. We design different evaluation metrics, image datasets, and several different image post-processing operations as a part of the framework. Secondly, we build several image forensic tools based on selected algorithms for detecting copy-move forgeries, re-sampling artifacts, and manipulations in JPEG images. The effectiveness and robustness of the tools are evaluated by using the developed test framework. Thirdly, for each selected technique, we develop several targeted attacks. The aim of targeted attacks against a forensic technique is to remove forensic evidence present in forged images. Subsequently, by using the test framework and the targeted attacks, we can thoroughly evaluate the security of the forensic technique. We show that image forensic techniques are often sensitive and can be defeated when their algorithms are publicly known. Finally, we develop new forensic techniques which achieve higher security in comparison with state-of-the-art forensic techniques