File Type Identification - Computational Intelligence for Digital Forensics

In modern world, the use of digital devices for leisure or professional reasons is growing quickly; nevertheless, criminals try to fool authorities and hide evidence in a computer by changing the file type. File type detection is a very demanding task for a digital forensic examiner. In this paper, a new methodology is proposed – in a digital forensics perspective- to identify altered file types with high accuracy by employing computational intelligence techniques. The proposed methodology is applied to the three most common image file types (jpg, png and gif) as well as to uncompressed tiff images. A three-stage process involving feature extraction (Byte Frequency Distribution), feature selection (genetic algorithm) and classification (neural network) is proposed. Experimental results were conducted having files altered in a digital forensics perspective and the results are presented. The proposed model shows very high and exceptional accuracy in file type identification

ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019

ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

This paper presents an overview of the ImageCLEF 2019 lab, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data, (ii) a lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (iii) a new security task addressing the problems of automatically identifying forged content and retrieve hidden information, and (iv) a new coral task about segmenting and labeling collections of coral images for 3D modeling. The strong participation, with 235 research groups registering, and 63 submitting over 359 runs, shows an important interest in this benchmark campaign

Στεγανάλυση εικόνων στη ψηφιακή εγκληματολογία

Nowadays, steganography is the main mean of illegal secret communication. Therefore, the need of detecting steganographic content and especially stego images is becoming more compulsory. However, steganalysis is a very difficult task and its success depends on many factors, like the presence of the cover medium, evidence of the utilized steganographic algorithm etc. Early steganalysis methods deploy statistical attacks on stego images while more recent ones use deep learning techniques. The latter ones mainly utilize convolutional neural networks and show promising results. This dissertation deals with issues related to steganalysis and in particular to image steganalysis. Βasic concepts of image steganalysis along with a taxonomy for classification of the different steganalysis methods used by a digital forensic examiner are presented. Moreover, a detailed overview of state-of-the-art methods proposed in literature is given. The research focuses in two major research questions i.e. the proposal of a novel convolutional neural network, and afterwards its utilization as a feature extractor. The proposed method initially utilized a dilated convolutional neural network - KarNet - to identify stego images from two different steganographic algorithms i.e. Spatial-Universal Wavelet Relative Distortion (S-UNIWARD) and Wavelet Obtained Weights (WOW). The proposed convolutional neural network was compared against other state-of-the-art deep learning techniques and it outperforms them.Στην σημερινή εποχή, η στεγανογραφία είναι ο κύριος τρόπος για την επίτευξη παράνομης μυστικής επικοινωνίας. Ως εκ τούτου, η ανάγκη ανίχνευσης στεγανογραφικού περιεχομένου και ιδίως στεγανογραφημένων εικόνων γίνεται επιτακτική. Ωστόσο, η στεγανάλυση είναι ένα πολύ δύσκολο έργο και η επιτυχία της εξαρτάται από πολλούς παράγοντες, όπως η παρουσία του μέσου στεγανογράφησης, τα αποδεικτικά στοιχεία του χρησιμοποιούμενου στεγνογραφικού αλγορίθμου κ.λπ. Οι πιο συνηθισμένες μέθοδοι στεγανάλυσης χρησιμοποιούν στατιστικά μέτρα για να αναγνωρίσουν στεγανογραφημένες εικόνες, ενώ οι πιο πρόσφατες χρησιμοποιούν τεχνικές βαθιάς μάθησης (deep learning). Οι τελευταίες χρησιμοποιούν κυρίως συνελικτικά νευρωνικά δίκτυα και παρουσιάζουν υποσχόμενα αποτελέσματα. Αυτή η διατριβή ασχολείται με ζητήματα που σχετίζονται με τη στεγανάλυση και ειδικότερα με τη στεγανάλυση εικόνων. Παρουσιάζονται οι βασικές έννοιες της στεγανάλυσης εικόνων μαζί με μια ταξινόμηση των διαφορετικών μεθόδων στεγανάλυσης που χρησιμοποιούνται από έναν εξεταστή ψηφιακών πειστηρίων. Επιπλέον, παρέχεται μια λεπτομερής επισκόπηση των προηγμένων μεθόδων που προτείνονται στη βιβλιογραφία. Η έρευνα επικεντρώνεται σε δύο μεγάλα ερευνητικά ερωτήματα, δηλαδή την πρόταση ενός νέου συνελικτικού νευρωνικού δικτύου και στη συνέχεια τη χρήση του ως εξαγωγέα χαρακτηριστικών. Η προτεινόμενη μέθοδος χρησιμοποιεί αρχικά ένα καινοτόμο συνελικτικό νευρωνικό δίκτυο - KarNet - για τον εντοπισμό στεγανογραφημένων εικόνων από δύο διαφορετικούς αλγόριθμους στεγανογραφίας, τους Spatial-Universal Wavelet Relative Distortion (S-UNIWARD) και Wavelet Obained Weights (WOW). Το προτεινόμενο συνελικτικό νευρωνικό δίκτυο συγκρίθηκε με άλλες προηγμένες τεχνικές βαθιάς μάθησης και τις ξεπερνά

A comprehensive survey of fingerprint presentation attack detection

Nowadays, the number of people that utilize either digital applications or machines is increasing exponentially. Therefore, trustworthy verification schemes are required to ensure security and to authenticate the identity of an individual. Since traditional passwords have become more vulnerable to attack, the need to adopt new verification schemes is now compulsory. Biometric traits have gained significant interest in this area in recent years due to their uniqueness, ease of use and development, user convenience and security. Biometric traits cannot be borrowed, stolen or forgotten like traditional passwords or RFID cards. Fingerprints represent one of the most utilized biometric factors. In contrast to popular opinion, fingerprint recognition is not an inviolable technique. Given that biometric authentication systems are now widely employed, fingerprint presentation attack detection has become crucial. In this review, we investigate fingerprint presentation attack detection by highlighting the recent advances in this field and addressing all the disadvantages of the utilization of fingerprints as a biometric authentication factor. Both hardware- and software-based state-of-the-art methods are thoroughly presented and analyzed for identifying real fingerprints from artificial ones to help researchers to design securer biometric systems

Overview of the ImageCLEFsecurity 2019: File Forgery Detection Tasks

The File Forgery Detection tasks is in its first edition, in 2019. This year, it is composed by three subtasks: a) Forged file discovery, b) Stego image discovery and c) Secret message discovery. The data set contained 6,400 images and pdf files, divided into 3 sets. There were 61 participants and the majority of them participated in all the subtasks. This highlights the major concern the scientific community shows for security issues and the importance of each subtask. Submissions varied from a) 8, b) 31 and c) 14 submissions for each subtask, respectively. Although the datasets were small, most of the participants used deep learning techniques, especially in subtasks 2 & 3. The results obtained in subtask 3-which was the most difficult one-showed that there is room for improvement, as more advanced techniques are needed to achieve better results. Deep learning techniques adopted by many researchers is a preamble in that direction, and proved that they may provide a promising steganalysis tool to a digital forensics examiner