Passive Techniques for Detecting and Locating Manipulations in Digital Images

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, leída el 19-11-2020El numero de camaras digitales integradas en dispositivos moviles as como su uso en la vida cotidiana esta en continuo crecimiento. Diariamente gran cantidad de imagenes digitales, generadas o no por este tipo de dispositivos, circulan en Internet o son utilizadas como evidencias o pruebas en procesos judiciales. Como consecuencia, el analisis forense de imagenes digitales cobra importancia en multitud de situaciones de la vida real. El analisis forense de imagenes digitales se divide en dos grandes ramas: autenticidad de imagenes digitales e identificacion de la fuente de adquisicion de una imagen. La primera trata de discernir si una imagen ha sufrido algun procesamiento posterior al de su creacion, es decir, que no haya sido manipulada. La segunda pretende identificar el dispositivo que genero la imagen digital. La verificacion de la autenticidad de imagenes digitales se puedellevar a cabo mediante tecnicas activas y tecnicas pasivas de analisis forense. Las tecnicas activas se fundamentan en que las imagenes digitales cuentan con \marcas" presentes desde su creacion, de forma que cualquier tipo de alteracion que se realice con posterioridad a su generacion, modificara las mismas, y, por tanto, permitiran detectar si ha existido un posible post-proceso o manipulacion...The number of digital cameras integrated into mobile devices as well as their use in everyday life is continuously growing. Every day a large number of digital images, whether generated by this type of device or not, circulate on the Internet or are used as evidence in legal proceedings. Consequently, the forensic analysis of digital images becomes important in many real-life situations. Forensic analysis of digital images is divided into two main branches: authenticity of digital images and identi cation of the source of acquisition of an image. The first attempts to discern whether an image has undergone any processing subsequent to its creation, i.e. that it has not been manipulated. The second aims to identify the device that generated the digital image. Verification of the authenticity of digital images can be carried out using both active and passive forensic analysis techniques. The active techniques are based on the fact that the digital images have "marks"present since their creation so that any type of alteration made after their generation will modify them, and therefore will allow detection if there has been any possible post-processing or manipulation. On the other hand, passive techniques perform the analysis of authenticity by extracting characteristics from the image...Fac. de InformáticaTRUEunpu

An improved discrete cosine transformation block based scheme for copy-move image forgery detection

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF

Copy-move forgery detection using convolutional neural network and K-mean clustering

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms

Image forgery detection using textural features and deep learning

La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses. Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification. De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur. Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation. Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models. Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset

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