A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewedA semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote sensing images applying a tree structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of themmultispectral or hyperspectral image is used to embed the mark in it order to detect any significant modification of the original image. The image is segmented into threedimensional blocks and a tree structured vector quantizer is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.Se presenta un esquema de marcas de agua semi-frágiles para múltiples imágenes de banda. Proponemos incorporar una marca en imágenes de detección remota, aplicando un enfoque de cuantización del vector de árbol estructurado con las definiciones de píxel, en lugar de procesar cada banda por separado. La firma de la imagen hiperespectral se utiliza para insertar la marca en el mismo orden para detectar cualquier modificación significativa de la imagen original. La imagen es segmentada en bloques tridimensionales y un cuantificador de vector de estructura de árbol se construye para cada bloque. Estos árboles son manipulados utilizando un algoritmo iteractivo hasta que el bloque resultante satisface un criterio necesario que establece la marca incrustada. El método se muestra para poder preservar la marca bajo compresión con pérdida (por encima de un umbral establecido) pero, al mismo tiempo, detecta posiblemente bloques forjados y su posición en la imagen entera.Es presenta un esquema de marques d'aigua semi-fràgils per a múltiples imatges de banda. Proposem incorporar una marca en imatges de detecció remota, aplicant un enfocament de quantització del vector d'arbre estructurat amb les definicions de píxel, en lloc de processar cada banda per separat. La signatura de la imatge hiperespectral s'utilitza per inserir la marca en el mateix ordre per detectar qualsevol modificació significativa de la imatge original. La imatge és segmentada en blocs tridimensionals i un quantificador de vector d'estructura d'arbre es construeix per a cada bloc. Aquests arbres són manipulats utilitzant un algoritme iteractiu fins que el bloc resultant satisfà un criteri necessari que estableix la marca incrustada. El mètode es mostra per poder preservar la marca sota compressió amb pèrdua (per sobre d'un llindar establert) però, al mateix temps, detecta possiblement blocs forjats i la seva posició en la imatge sencera

Provenance analysis for instagram photos

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than 96% in differentiating the filters in Group I and Group II

La falsificazione epigrafica. Questioni di metodo e casi di studio

This paper aims to reconsider the manuscript by Jacopo Valvasone (1499-1570), formerly owned by the Earl of Leicester (now British Library, Additional MS 49369), which Theodor Mommsen borrowed and inspected in 1876, just before the publication of the second part of CIL V. In the letter that he wrote to thank the Vicar and Librarian of Halkham Hall, Mommsen declared that Valvasone joined \u201cthe the long list of forgers\u201d. The analysis of forgeries in Valvasone\u2019s manuscript could show whether Mommsen was right in his opinion

SIFT match removal and keypoint preservation through dominant orientation shift

In Image Forensics, very often, copy-move attack is countered by resorting at instruments based on matching local features descriptors, usually SIFT. On the other side, to overcome such techniques, smart hackers can try firstly to remove keypoints before performing image patch cloning in order to inhibit the successive matching operation. However, keypoint removal determines per se some suspicious empty areas that could indicate that a manipulation has occurred. In this paper, the goal to nullify SIFT matches while preserving keypoints is pursued. The basic idea is to succeed in altering the features descriptor by means of shifting the dominant orientation associated to a specific keypoint. In fact, to provide rotation invariance, all the values of the descriptor are computed according to such orientation. So doing, it should impair the whole matching phase. © 2015 EURASIP

An analysis on attacker actions in fingerprint-copy attack in source camera identification

Multimedia forensics deals with the analysis of multimedia data to gather information on their origin and authenticity through the use of specific tools. At this point an important question arises: how much reliable are these algorithms? In this work we have considered the technique presented in [1] where it is shown how source camera identification can be attacked. In particular, the problem investigated concerns the situation when an adversary estimates the sensor fingerprint from a set of images belonging to the person he wants to frame and superimposes it onto an image acquired by a different camera to charge the innocent victim as the author of that photo. In [1], a countermeasure against such attack, named Triangle Test, is introduced. In this paper we have analyzed if a more sophisticated action of the attacker can invalidate such countermeasure. Experimental results are provided to prove how hacker's action could be improved

Distinguishing between camera and scanned images by means of frequency analysis

Distinguishing the kind of sensor which has acquired a digital image could be crucial in many scenarios where digital forensic techniques are called to give answers. In this paper a new methodology which permits to determine if a digital photo has been taken by a camera or has been scanned by a scanner is proposed. Such a technique exploits the specific geometrical features of the sensor pattern noise introduced by the sensor in both cases and by resorting to a frequency analysis can infer if a periodicity is present and consequently which is the origin of the digital content. Experimental results are presented to support the theoretical framework

Fast image clustering of unknown source images

Succeeding in determining information about the origin of a digital image is a basic issue of multimedia forensics. In particular it could be interesting to individuate which is the specific camera (brand and/or model) that has taken that photo; to do that, additional knowledge are needed about the camera such as its fingerprint, usually computed by resorting at the extraction of the PRNU (Photo-Response-Uniformity-Noise) by using a group of images coming from that camera. It is easy to understand that in many application scenarios information at disposal are very limited; this is the case when, given a set of N images, we want to establish if they belong to M different cameras where M is less or, at most, equal to N, without having any knowledge about the source cameras. In this paper a new technique which aims at blindly clustering a given set of N digital images is presented. Such a technique is based on a pre-existing one [1] and improves it both in terms of error probability and of computational efficiency. The system is able, in an unsupervised and fast manner, to group photos without any initial information about their membership. Sensor pattern noise is extracted by each image as reference and the successive classification is performed by means of a hierarchical clustering procedure. Experimental results have been carried out to verify theoretical expectations and to witness the improvements with respect to the other technique. Tests have also been done in different operative circumstances (e.g. asymmetric distribution of the images within each cluster), obtaining satisfactory results