Sicurezza e Giustizia

IMAGE/VIDEO FORENSICS: THEORETICAL BACKGROUND, METHODS AND BEST PRACTICES – Part two: From analog to digital world

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by Fausto Galvan and Sebastiano Battiato

From the beginning of this century, Image/Video Forensics experts faced the need to extract the largest number of information from a digital visual content, developing a plethora of methods and algorithms. These approaches, which may concern the authentication of images or videos, the identification of the device in which the visual data was originated, or the alterations to which the document has been subjected, find applications both in the civil and criminal context. In a series of three papers, we provide first an introductory part about the powerful impact of images and videos in today’s reality, followed by a section where we highlight the differences between the analog and digital age in the formation of an image. Then we will define what is a digital evidence, and we will introduce Image/Video Forensics as a branch of the forensic sciences, highlighting its potential and limits. In the following, we will examine in detail some methods allowing to retrieve information from images when they are not readily available, and finally will provided a list of free and non-free software to face the daily challenges coming from processing images and videos for forensic purposes. The work ends with a list of publications containing the Best Practices in the field.

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1. Images from analog to digital world
Before the advent of digital photography, the authenticity of an image presented as evidence in a trial was very rarely questioned. Nowadays, on the contrary, the risk of dealing with manipulated images is very high (Battiato and Galvan, 2013), and image forgeries can be classified into three categories:

  1. Image processing using computer graphics (GC) methods (e.g. artificially generated / modified objects or details);
  2. Alteration of the image meaning, without modifying its content (e.g. color variations and / or brightness, resizing);
  3. Altering the image content, inserting (e.g. copying and pasting) or eliminating (e.g. cropping, deleting) significant parts.

Before examining in detail the various approaches used to check the originality of an image, is important to understand the reason why, at the present time, our confidence in this kind of documents is so diminished. To this aim, it’s useful to highlight the differences in the image formation pipeline between the analogical and current ages.

In analog cameras, the image was formed on a thin strip of plastic on which other layers of different materials are superimposed, in quantities and composition depending on technical choices. The copies generated starting from the impressed film (which can be thought as a sort of “matrix” of the image), were identical to each other, excepted for slight chromatic variations determined by the dosages of some reagents. Everytime it was necessary joining the file of an investigation with the related photographs, it was used providing the entire original film, the so-called “negatives” of the images. This last measure was considered enough to avoid any complaints about the originality of that kind of proof. Possessing the entire original film not only assured of having the original images, but furthermore preventing the risk that some “inconvenient” clues could be erased, since every image in the film was marked in chronological order. Actually, even negatives could be altered, as we saw in Figure 1 in the first part of this set of three papers. The approaches to do this were mainly two: acting physically on the film, removing or adding some parts and then extracting the image from the modified negative, or duplicating the negative with a special instrumentation, applying appropriate masks suitable for hide or insert the desired details. In both methods, however, the changes were easily detectable by an expert eye: in the first case examining the modified negative, in the latter leveraging the different characteristics (grain, thickness) of the negative-copy, which for technical reasons were never the same as those of camera rollers.

In today’s cameras, from a functional point of view, the analogue of the thin strip of plastic where the image was impressed, is the sensor. In fact, on this thin slice of silicon the information upon the brightness of the scene is stored as captured by the corresponding photoreceptors. In detail, the image formation pipeline in modern devices follows the path exposed in Figure 1: the light coming from the physical world passes through a (more or less complex) system of lens, then through the Color Filter Array, and in the electronic sensor, where the conversion of light to pixel values succeeds. Before this step is completed, it has to undergone to some regulation and clearing inner software, which varies upon the various models. On the sensor, therefore, the image is present for a very short period of time, before being stored in the device’s memory.

 

2. Digital footprint as a source of evidence
Digital evidence belongs to the group of the scientific evidences, defined as: evidences that are provided by some scientific-technical tool with the addition of specific technical skills, possibly with the intervention of an expert in the specific field (AA.VV., 2008), in particular to the scientific area denoted as Digital Forensics. This science includes many subareas, as exposed in Figure 2, and can in turn be described in two ways, with respect to different important aspects:

  • The purposes: the use of scientifically derived and proven methods toward the preservation, collection, validation, identification, analysis, interpretation, documentation and presentation of digital evidence derived from digital sources for the purpose of facilitation or furthering the reconstruction of events found to be criminal, or helping to anticipate unauthorized actions shown to be disruptive to planned operations (Beebe, 2009).
  • The intervention of an expert: the science of locating; extracting and analyzing types of data from different devices, which are interpreted by specialists in order to be used as legal evidence (Fenu and Solinas, 2013).
  • The following formal definition of digital evidence (Carrier, 2003) is widely accepted: digital data that establish that a crime has been committed can provide a link between a crime and its victim, or between a crime and the perpetrator. From a practical point of view, a digital evidence is composed by a digital content, often stored as a file of whatever format, and characterized by the following attributes:
    • Volatility, such as residues of gunpowder. Think about a chat with an internet browser setted in private browsing;
    • Latency, such as fingerprints or a DNA evidence. This is the case of data that have been erased or hidden (like steganography);
    • Easy to modify or to spoil since reliability of digital data are intrinsically fragile. Indeed, with a simple copy-paste operation could affect the strength of a digital evidence.

 

3. Limits and potentialities of Image / Video Forensics
As a part of Digital Forensics, Forensic Analysis of Images , also known as Image Forensics, is a forensic science carried out since the very first photos were made. From FBI web site (www.fbi.gov), we could read that “Forensic Image Analysis is the application of image science and domain expertise to interpret the content of an image or the image itself in legal matters”. From this definition, we can identify the main points that should characterize every forensics approach to the analysis of an image: it must be provided by someone with adequate technical skills, but also able to interpret the extracted information inside a legal and judicial framework. Image/Video Forensics methods are grouped in six main categories (Redi, Taktak and Dugelay, 2011 – Piva, 2013):

  1. Image forgery identification: Modeling the path of light during image creation reveals physical, geometric, and statistical regularities that are destroyed during the creation of a fake. Various forensic techniques exploit these irregularities to detect traces of tampering. These methods are further divided in Pixel Based (to detect cloning, resampling and splicing operations), Statistical Based, Format Based (to detect, for example, single or multiple JPEG compressions), Camera Based (that specifically model artifacts introduced by various stages of the imaging process), Physics Based (that leverage the inconsistencies introduced in the tampered image when its parts are coming by different environments), and finally Geometric Based (aimed to find inconsistencies connected to the formation of the image inside the camera) (Farid, 2016).
  2. Source camera identification: identification of the device (hopefully the exact one, more often the brand of the device) that generated the image. Sometimes the first step is devoted to discriminate between natural or artificial (also known as Computer Generated) images. In general, the methodology switches to the identification of the source that generated the image, ascertaining the type of device (scanner, camera, copier, and printer) and then trying to determine the particular device.
  3. Image reconstruction/restoration/enhancement: restoration and improvement of the quality of deteriorated images in order to identify, even partially, the original content and/or retrieve useful information (Gonzalez and Woods, 2002).
  4. Image/video analysis: dynamic or behavioral analysis, for example with the aim to identify the consecutio temporum of an event of interest.
    3D reconstruction and comparison: bi/three-dimensional information extraction to derive measures or reference values (for example the height of an individual) and for the comparison between images (for example to compare the identity of a subject with the known offender from a footage taken by a video surveillance system).
  5. Steganalysis: detection of hidden information within an image with steganographic techniques, for example by changing the least significant bit in the number that defines the color of a pixel (LSB approach).

 

The first two of the above list are the one more closely connected to forensics issues, whereas the others are general-purposes approaches. Nonetheless, the other areas are equally relevant both in the field of security (e.g. video surveillance) and for more traditional investigative purposes, when simply it is necessary to highlight details and information contained in images. However, we want to point out that such methodologies of analysis can be used to extract the relevant forensic evidence only if the information is present (although apparently in a few amount, as in Figure 4). It may seem trivial to highlight this aspect, but very often this kind of analysis is asked by someone who does not accept the fact that information is actually absent. An example in this sense (unfortunately still very common) concerns images acquired by video surveillance devices, which, despite recording the criminal event, are unusable due to the poor quality of the recovery system. In certain cases, data simply doesn’t exist, and can’t be “invented”. Clear examples of what an Image/Video Forensics will not never be able to do is the so-called “CSI effects”, well represented online by video as the one available at: https://www.youtube.com/watch?v=Vxq9yj2pVWk: impossible zooms, 3D reconstructions without any scientific basis, and so on.

In the following, we listed some typical examples of questions that may be posed by the prosecutors to law enforcement agencies in the various steps of an investigation:

  • Image Enhancement: Is it possible to improve the image/video in order to extract the license plate?
  • Image Autentication: Did the images/videos undergone to some alterations or are they authentic, with reference to the time of shooting?
  • Source Identification – 1: Can we state that the images/videos come from the seized device?
  • Source Identification – 2: Do the images / videos come from a device as camera or videorecorder (which means, shooting a real scene), or are they produced using Computer Graphics (CG) methods?

 

3. Extraction of plates and heights: reconstruction of 2D and 3D scenes from images
Using proper algorithms, which leverage some mathematical tools, it is possible to extract details of an image which are not available at a first sight, and, under certain conditions, even reconstructing with great precision the 3D representation of the scene from where the two-dimensional image came from.

According to the classic Pinhole Camera model, (Criminisi, 2002) an X point in the 3D world is projected (mathematically, “mapped”) in the two-dimensional plane of the image in a corresponding point x. This is the intersection of the image plane with the line segment that joins the optical center O of the camera and the X point of the shot scene (Figure 3). The algebraic interpretation of this projection is summarized by the following simple equation:

x=PX (1)

where P is a mathematical operator called “projection matrix”, a table whose coefficients define the rules for the transformation of the real points into image points. If we know (or we are able to reconstruct in any way) the matrix P, we have the possibility of reverse this transformation. In other words, starting from a point in the image, we can determine its position within the real scene that the image reproduces.
In practice, it is not always possible completely rebuilding the projection matrix, but under certain conditions, or when information on the camera that shot the image (step known as Calibration) are avoidable, and/or some measures of the objects portrayed in the image, the number of unknowns needed to define the matrix is greatly reduced. An useful example of this last condition is in case of the so called Rectification, when the object being photographed is itself 2D. In Figure 4 an inversion of this type has been applied to an image in which we want to highlight the numbers of a car license plate.

 

Working on three-dimensional objects, if we need to estimate the height of a subject, it is necessary first of all retrieving the real measurements of objects or elements present in the image (which can be recovered also afterwards). For example, Law Enforcement operators can return to the place where the photo was taken to manually detect the dimensions of a door, a window, or the wall of a house.

 

Otherwise, this information can be derived from appropriate documentation, if present. The second step to be taken, consists in extracting from the image a series of characteristics known as vanishing points and lines (Figure 5). In literature there are a lot of algorithms allowing to estimate these geometric entities directly from the image, without knowing the intrinsic parameters (e.g. concerning the internal settings of the camera) or extrinsic ones (concerning the positioning of the camera relative to the scene) (Szeliski, 2010).

Searching for information in images, sometimes we can go far beyond the simple extrapolation of single measures, like heights of people or objects. Indeed, increasing the number of “real” measures detected, it is possible to extend the above methods up to reconstructing the entire 3D scene from which the image is taken, as well as the position of the camera at the time of shooting. An example is shown in Figure 6, where the heights of the main window frame, of a column, and the dimensions of the two sides of the porch base were used as reference dimensions (Criminisi, 2002).
We want to point out that, like all estimation methods, the processes described above are subject to errors that may derive from multiple sources: the incorrect collection of reference measurements, an image affected by distortions (e.g. lens distortion, blurred, poor definition), subject not perfectly vertical, etc. In general, however, it is possible to obtain a good estimate, together with a known and measurable error level.

Beyond the application to biometry, in a forensics scenario these approaches may be useful also for Image Forgery Detection, since the discrepancies between geometric clues are sought in different parts of the photo to highlight possible manipulations (Johnson and Farid, 2007 – Wu and Wang, 2011). In the paper, leveraging the estimate of the principal point of the camera (the projection of the optical center on the image plane), authors are able to highlight the non-originality of the photo. Indeed, in case of an authentic image this point is close to the center of the photo image for every subject, whereas in case of a forgery made by image splycing, as can be appreciated in Figure 7, the principal point of a subject artificially inserted in the image would result away from the same points relative to the other subjects of the scene. ©

 

REFERENCES

  • S. Battiato, F. Galvan: La validità probatoria di immagini e video. Sicurezza e Giustizia – II, pp. 30:31, 2013.
  • AA.VV.: Enciclopedia del Diritto. Giuffrè Editore, 2008.
  • N. Beebe: Digital forensic research: The good, the bad and the unaddressed. Advances in Digital Forensics V, pages 17–36. Springer, 2009.
  • G. Fenu and F. Solinas: Computer forensics between the italian legislation and pragmatic questions. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 2(1):9–24, 2013.
  • B. Carrier, E. H. Spafford: Getting physical with the digital investigation process. International Journal of digital evidence, 2(2):1–20, 2003.
  • A. Piva: An overview on image forensics. ISRN Signal Processing, 2013.
  • J.A. Redi, W.Taktak, J.L. Dugelay: Digital image forensics: a booklet for beginners. Multimedia Tools Application – 51:133-162, 2011;
  • H. Farid: Photo forensics. MIT Press, 2016.
  • R. C. Gonzalez, E. R. Wood: Digital Image Processing. Prentice Hall Press, 4th Edition, 2018.
  • A. Criminisi: Single-view metrology: Algorithms and applications. Joint Pattern Recognition Symposium. Springer, Berlin, Heidelberg, 2002.
  • R. Szeliski: Computer Vision. Algorithms and Applications, Springer, 2010.
  • M.K. Johnson, H. Farid: Detecting Photographic Composites of People, 6th International Workshop on Digital Watermarking, Guangzhou, China, 2007.
  • L. Wu,Y. Wang: Detecting Image Forgeries using Geometric Cues. Chapter in Computer Vision for Multimedia Applications: Methods and Solutions, 2011.

 


Altri articoli di Sebastiano Battiato

IMAGE/VIDEO FORENSICS: THEORETICAL BACKGROUND, METHODS AND BEST PRACTICES – Part one: Can we trust Images and Videos?
by Fausto Galvan and Sebastiano Battiato (N. IV_MMXVIII)
From the beginning of this century, Image/Video Forensics experts faced the need to extract the largest number of information from a digital visual content, developing a plethora of methods and algorithms. These approaches, which may concern the authentication of images or videos, the identification of the device in which the visual data was originated, or the alterations to which the document has been subjected, find applications both in the civil and criminal context. In a series of three papers, we provide first an introductory part about the powerful impact of images and videos in today’s reality, followed by a section where we highlight the differences between the analog and digital age in the formation of an image. Then we will define what is a digital evidence, and we will introduce Image/Video Forensics as a branch of the forensic sciences, highlighting its potential and limits. In the following, we will examine in detail some methods allowing to retrieve information from images when they are not readily available, and finally will provided a list of free and non-free software to face the daily challenges coming from processing images and videos for forensic purposes. The work ends with a list of publications containing the Best Practices in the field.
RICOSTRUZIONE DI EVENTI E DI DINAMICHE ATTRAVERSO S.U.M.O., UN AVANZATO TOOL DI SIMULAZIONE DEL TRAFFICO URBANO
di Sebastiano Battiato, Oliver Giudice, Antonino Barbaro Paratore (N. III_MMXVIII)
L’ausilio di strumenti di simulazione informatica permette una più completa e per quanto possibile esaustiva valutazione degli eventi grazie alla capacità intrinseca di tali strumenti di generare automaticamente centinaia di migliaia di eventi, al variare di tutte le combinazioni di variabili e situazioni incognite. A tale scopo in questo articolo si presenta il software opensource “Simulation of Urban Mobility” (SUMO).
ANALISI VIBRAZIONALE AVANZATA (AVA): UN ALGORITMO INNOVATIVO PER L’ESECUZIONE DI INTERCETTAZIONI AMBIENTALI
di Francesco Rundo, Sebastiano Battiato, Sabrina Conoci e A. Luigi Di Stallo (N. I_MMXVIII)
Decreto legislativo 29 dicembre 2017, n. 216. Con decorrenza Gennaio 2018, entra in vigore la riforma della disciplina delle intercettazioni attuata con il decreto legislativo 29 dicembre 2017, n. 216: “Disposizioni in materia di intercettazioni di conversazioni o comunicazioni, in attuazione della delega di cui all’articolo 1, commi 82, 83 e 84, lettere a), b), c), d) ed e), della legge 23 giugno 2017, n. 103”. La riforma rafforza il ruolo delle intercettazioni come indispensabile strumento di indagine ed investigazione forense. Nell’ambito di questa riforma, gli autori intendono illustrare nel presente articolo, una innovativa pipeline di elaborazione dati che consente, sotto opportune ipotesi, la ricostruzione delle conversazioni tra due o piu’ soggetti, dal solo filmato video (a risoluzione e frame-rate tipici di uno dispositivo di acquisizione video di ultima generazione) senza avere accesso alla relativa traccia audio associata.
SISTEMI “POINT OF CARE” PER LE INDAGINI GENETICHE IN AMBITO FORENSE
di Sabrina Conoci , Salvatore Petralia, Francesco Rundo, Sebastiano Battiato (N. I_MMXVII)
Un’attenta disamina dei processi penali più recenti (alcuni dei quali divenuti “mediatici”) ha evidenziato il ruolo dirompente dell’analisi genetica e bio-molecolare nelle indagini investigative eseguite dagli inquirenti. Quando viene richiesta la repertazione di campioni biologici, le procedure adottate per il campionamento e successiva analisi delle tracce, devono rispettare standard di altissimo rigore scientifico grazie ai quali è poi possibile garantire l’accuratezza, la ripetibilità e l’assenza di contaminazione dovuta ad una non conforme procedura di repertazione o ad una errata catena di custodia del reperto. Nell’articolo che segue, gli autori illustreranno le potenzialità dei sistemi c.d. “Point of Care”(PoC) genetici” sia in riferimento all’indagine genetica in ambito forense che, in generale, come strumento per l’analisi in loco di reperti biologici rinvenuti nella scena di un crimine. Il “PoC genetico” può essere definito come sistema in grado di eseguire il processo diagnostico sample-in-answer-out senza intervento di un complesso laboratorio analitico. Esso è costituito in concreto dalla combinazione tra un sistema di detezione per il riconoscimento della composizione genetica e bio-molecolare del campione, da un sistema di trasduzione ottica od elettrica del segnale e da un sistema di post-processing dei dati, che si avvale di algortimi di interpretazione “immediata” delle rilevazioni acquisite, le quali produrrano in concreto report sulla profilazione del DNA campionato.
LA STIMA DELL’ERRORE NELLA DETERMINAZIONE DELL’ALTEZZA DI UN SOGGETTO RIPRESO DA UN SISTEMA DI VIDEOSORVEGLIANZA
di Sebastiano Battiato e Giovanni Tessitore (N. IV_MMXVI)
La diffusione dei sistemi di video-sorveglianza pubblici e privati rende frequente oggigiorno il caso in cui gli autori di un reato siano ripresi dalle telecamere di tali sistemi. La misura dell’altezza può essere un valido strumento per restringere la cerchia dei sospettati ed aiutare ad identificare i soggetti coinvolti. La stima dell’errore di misura deve sempre accompagnare le misure effettuate.
MEDICAL IMAGE ENHANCEMENT NEI PROCEDIMENTI GIUDIZIARI MEDICO-LEGALI IN AMBITO ONCOLOGICO
di Francesco Rundo, Edoardo Tusa, Sebastiano Battiato (N. I_MMXVI)
La professione medica e sanitaria riveste un ruolo cruciale nel tessuto sociale ed economico del nostro Paese, attese peraltro le numerose vicende giudiziarie che hanno costretto, non da ultimo, il legislatore ad innovare nuovamente il quadro normativo che regolamenta e disciplina le azioni giudiziarie per errore medico. In questo quadro piuttosto complesso in cui si contrappongono gli interessi dei pazienti a quelli della professione medico-sanitaria è certamente attuale la figura del consulente tecnico (sia di parte che d’ufficio) che nei contenziosi medico-legali ha l’arduo compito di valutare con giudizio e rigore scientifico l’operato del professionista medico chiamato a rispondere del proprio operato. L’obiettivo del consulente tecnico è, dunque, quello di indirizzare opportunamente l’adito giudicante al fine di discriminare con ragionevole certezza l’errore volontario, doloso o colposo commesso dal professionista per imperizia, negligenza, superficialità ovvero per mancata adesione alle linee guida adottate dalla comunita scientifica, dagli scenari in cui nonostante il risultato avverso per il paziente questo non sia imputabile al professionista medico che ha , dunque, esercitato la sua professione al meglio delle proprie possibilità difettando, in concreto, il c.d. nesso eziologico. Nell’articolo proposto si mostreranno, attraverso la presentazione di un caso-studio in ambito oncologico, gli enormi vantaggi che la costituzione di un team di consulenza multi-disciplinare composto oltre che da medici, anche da ingegneri e matematici, può apportare nei contenziosi medico-legali sia in ambito civile che penale. Nello specifico, il team multi-disciplinare costituito dagli autori (medico-legale, ingegnere, informatico, matematico) attraverso un’analisi rigorosa delle immagini mediche riferite al caso presentato, messe peraltro in relazione con le linee guida adottate dalla comunita medica, ha sensibilmente elevato il livello di accuratezza scientifica della valutazione medico-legale del caso esaminato con l’ovvia conseguenza in relazione al peso probatorio che una tale analisi avrà in sede giudiziaria qualora il paziente decida di adire le vie legali.
VERIFICA DELL’ATTENDIBILITÀ DI UN ALIBI COSTITUITO DA IMMAGINI O VIDEO
di Sebastiano Battiato e Fausto Galvan (N. II_MMXIV)
Tra le conseguenze che l’informatizzazione globale ha sul mondo delle investigazioni, vi è il sempre maggior utilizzo di strumenti digitali per la creazione di alibi. In questo contesto, le immagini e/o i video opportunamente modificati sono molto sfruttati. Al fine di rivelare le manipolazioni dei documenti visivi, oltre ai metodi forniti dalla Image Forensics “classica” vengono qui considerati, in una visione più globale del fenomeno falsificatorio, gli approcci che analizzano la possibile presenza di falsi originali. Alcuni esempi tratti da casi reali completano la trattazione.
RICOSTRUZIONE DI INFORMAZIONI 3D A PARTIRE DA IMMAGINI BIDIMENSIONALI
di Sebastiano Battiato e Fausto Galvan ( n.IV_MMXIII )
Tra gli strumenti di indagine disponibili oggigiorno, cominciano a farsi strada anche metodi che permettono di estrapolare da una (o più) immagini informazioni relative alle dimensione di oggetti e/o persone fotografate. Alcuni di questi algoritmi rendono possibile ricostruire l’intera scena 3D ripresa dalla camera al momento dello scatto. Dopo una breve introduzione alla teoria matematica di riferimento verranno brevemente elencati alcuni risultati di rilievo già utilizzati ampiamente in ambito investigativo.
LA VALIDITÀ PROBATORIA DELLE IMMAGINI E DEI VIDEO
di Sebastiano Battiato e Fausto Galvan ( n.II_MMXIII )
Il numero di immagini in circolazione sul web, e non solo, è in costante aumento. Questo scenario ha un inevitabile riscontro in ambito forense: è sempre più improbabile che un evento delittuoso possa consumarsi senza che la scena del crimine o parte di essa, oppure l’autore del fatto, non vengano ripresi da un sistema di videosorveglianza. La relativa facilità con cui al giorno d’oggi l’uso di software di fotoritocco o di editing video, anche di facile reperimento, permette di “comporre” una immagine o “montare” una scena alterandone i contenuti originari impone che l’acquisizione ed il trattamento di immagini e video digitali sia regolato da “best practice” di riferimento.

 


Altri articoli di Fausto Galvan

IMAGE/VIDEO FORENSICS: THEORETICAL BACKGROUND, METHODS AND BEST PRACTICES – Part one: Can we trust Images and Videos?
by Fausto Galvan and Sebastiano Battiato (N. IV_MMXVIII)
From the beginning of this century, Image/Video Forensics experts faced the need to extract the largest number of information from a digital visual content, developing a plethora of methods and algorithms. These approaches, which may concern the authentication of images or videos, the identification of the device in which the visual data was originated, or the alterations to which the document has been subjected, find applications both in the civil and criminal context. In a series of three papers, we provide first an introductory part about the powerful impact of images and videos in today’s reality, followed by a section where we highlight the differences between the analog and digital age in the formation of an image. Then we will define what is a digital evidence, and we will introduce Image/Video Forensics as a branch of the forensic sciences, highlighting its potential and limits. In the following, we will examine in detail some methods allowing to retrieve information from images when they are not readily available, and finally will provided a list of free and non-free software to face the daily challenges coming from processing images and videos for forensic purposes. The work ends with a list of publications containing the Best Practices in the field.
VERIFICA DELL’ATTENDIBILITÀ DI UN ALIBI COSTITUITO DA IMMAGINI O VIDEO
di Sebastiano Battiato e Fausto Galvan (N. II_MMXIV)
Tra le conseguenze che l’informatizzazione globale ha sul mondo delle investigazioni, vi è il sempre maggior utilizzo di strumenti digitali per la creazione di alibi. In questo contesto, le immagini e/o i video opportunamente modificati sono molto sfruttati. Al fine di rivelare le manipolazioni dei documenti visivi, oltre ai metodi forniti dalla Image Forensics “classica” vengono qui considerati, in una visione più globale del fenomeno falsificatorio, gli approcci che analizzano la possibile presenza di falsi originali. Alcuni esempi tratti da casi reali completano la trattazione.
RICOSTRUZIONE DI INFORMAZIONI 3D A PARTIRE DA IMMAGINI BIDIMENSIONALI
di Sebastiano Battiato e Fausto Galvan ( n.IV_MMXIII )
Tra gli strumenti di indagine disponibili oggigiorno, cominciano a farsi strada anche metodi che permettono di estrapolare da una (o più) immagini informazioni relative alle dimensione di oggetti e/o persone fotografate. Alcuni di questi algoritmi rendono possibile ricostruire l’intera scena 3D ripresa dalla camera al momento dello scatto. Dopo una breve introduzione alla teoria matematica di riferimento verranno brevemente elencati alcuni risultati di rilievo già utilizzati ampiamente in ambito investigativo.
LA VALIDITÀ PROBATORIA DELLE IMMAGINI E DEI VIDEO
di Sebastiano Battiato e Fausto Galvan ( n.II_MMXIII )
Il numero di immagini in circolazione sul web, e non solo, è in costante aumento. Questo scenario ha un inevitabile riscontro in ambito forense: è sempre più improbabile che un evento delittuoso possa consumarsi senza che la scena del crimine o parte di essa, oppure l’autore del fatto, non vengano ripresi da un sistema di videosorveglianza. La relativa facilità con cui al giorno d’oggi l’uso di software di fotoritocco o di editing video, anche di facile reperimento, permette di “comporre” una immagine o “montare” una scena alterandone i contenuti originari impone che l’acquisizione ed il trattamento di immagini e video digitali sia regolato da “best practice” di riferimento.