Deep neural networks based error level analysis for lossless image compression based forgery detection.

The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the efficiency of distinguishing copy-move images produced by Deep Fake from the real ones. Error Level Analysis is used on images in-depth for identifying whether the photograph has long passed through changing. This Model uses CNN on the dataset of images for training and to test the dataset for identifying the forged image. Convolution neural network (CNN) can extract the counterfeit attribute and detect if images are false. In the proposed approach after the tests were carried out, it is displayed with the pie chart representation based on percentage the image is detected. It also detects different image compression ratios using the ELA process. The results of the assessments display the effectiveness of the proposed method

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

A Novel Approach to Detect Copy Move Forgery using Deep Learning

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire