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The term "mimicry attack" has been coined in computer security and used in adversarial machine learning: an attacker observes what a machine-learning system has learned and adjusts the malicious input so that it mimics a benign input. In this paper we extend this concept to image forensics, to allow an attacker modifying a manipulated image so that it appears pristine when analyzed by a target forensic detector. Recent work has shown that such attacks can be executed against detectors based on deep networks for hiding image tampering. We do more than that: our mimicry attack can force the target detector to identify arbitrary fictitious manipulations, while hiding the true ones. Accordingly, the user of the forensic detector is completely misled. From a methodological viewpoint, the proposed attack artificially alters the detector-specific intermediate representations according to the pixel distribution in the manipulated image, by applying a gradient-based optimization process. Experimental tests on different data sets and detectors demonstrate that our approach succeeds in jointly hiding manipulated areas and arbitrarily adding new ones, favorably comparing with the state-of-the-art in the first task. [Display omitted] • The proposed mimicry attack hides forgeries in images and introduces fictitious ones • It allows adversaries to introduce semantic biases and mislead the forensic detection • Its large-scale adoption may hinder the trust on forensic tools, akin to DoS attacks • Sequential detector-specific attacks enable tackling multiple detectors at once [ABSTRACT FROM AUTHOR]

Low-cost personal computers, wireless access technologies, the Internet, and computer-equipped classrooms allow the design of novel and complementary methodologies for teaching digital information security in electrical engineering curricula. The challenges of the current digital information era require experts who are effectively able to counteract piracy, forgery, copyright infringement, and so on. Digital watermarking is one possible technique for fighting piracy, which consists of the insertion of invisible but robust information to protect the data. In this paper, a new teaching approach, designed for testing student skills and progressing in multimedia data protection, is presented. This consists of a distributed security game where students compete by first using the developed watermarking techniques and then attacking each other's methods, thus verifying their robustness. Groups of students from different universities and countries play against each other, trying to compromise other teams' hiding systems while protecting their own data from attacks. The proposed methodology can be considered as an appealing approach for stimulating learning, cooperation, and team competition. The effectiveness of the teaching method is verified by a student survey and their academic results. [ABSTRACT FROM AUTHOR]

Imagine a future in which computer-generated (CG) human characters become almost indistinguishable from real humans. It is not that far away. Real-time rendering of photorealistic facial animation is just around the corner. To prepare for this world in which CG content will be intermingled with the natural, there is a fundamental need to understand how human beings can preserve the awareness of what is real and what is not. We present a methodology for distinguishing real and CG human faces within the context of multimedia forensics, by exploiting the heart rate as the discriminating feature. The underlying idea is that humans always present a pulse signal that can be automatically extracted from a video sequence depicting their face, while this is not the case for virtual humans. Experimental validation shows the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Quality of Experience of multimedia content is investigated to understand the overall acceptability of the content as perceived by the end user. Recent perspectives stress the need to focus not only on perceptual aspects of the content but also on the interaction between perception and cognitive–emotional aspects. Our contribution lies on this direction by presenting a contactless approach to estimate heart rate variability by evaluating face skin color variations in a video sequence. The proposed framework allows to analyze long video sequences depicting a subject and it is robust with respect to small movements and illumination changes. The system is validated on a dataset of 64 sequences where users observe 3D movies selected to induce heart rate variations. [ABSTRACT FROM AUTHOR]

Over the last few years, a rapid growth has been witnessed in the number of digital photos produced per year. This rapid process poses challenges in the organization and management of multimedia collections, and one viable solution consists of arranging the media on the basis of the underlying events. However, album-level annotation and the presence of irrelevant pictures in photo collections make event-based organization of personal photo albums a more challenging task. To tackle these challenges, in contrast to conventional approaches relying on supervised learning, we propose a pipeline for event recognition in personal photo collections relying on a multiple instance-learning (MIL) strategy. MIL is a modified form of supervised learning and fits well for such applications with weakly labeled data. The experimental evaluation of the proposed approach is carried out on two large-scale datasets including a self-collected and a benchmark dataset. On both, our approach significantly outperforms the existing state-of-the-art. [ABSTRACT FROM AUTHOR]

The creation of manipulated multimedia content involving human characters has reached in the last years unprecedented realism, calling for automated techniques to expose synthetically generated faces in images and videos. This work explores the analysis of spatio-temporal texture dynamics of the video signal, with the goal of characterizing and distinguishing real and fake sequences. We propose to build a binary decision on the joint analysis of multiple temporal segments and, in contrast to previous approaches, to exploit the textural dynamics of both the spatial and temporal dimensions. This is achieved through the use of Local Derivative Patterns on Three Orthogonal Planes (LDP-TOP), a compact feature representation known to be an important asset for the detection of face spoofing attacks. Experimental analyses on state-of-the-art datasets of manipulated videos show the discriminative power of such descriptors in separating real and fake sequences, and also identifying the creation method used. Linear Support Vector Machines (SVMs) are used which, despite the lower complexity, yield comparable performance to previously proposed deep models for fake content detection. [ABSTRACT FROM AUTHOR]

Research on event-based processing and analysis of media is receiving an increasing attention from the scientific community due to its relevance for an abundance of applications, from consumer video management and video surveillance to lifelogging and social media. Events have the ability to semantically encode relationships of different informational modalities, such as visual–audio–text, time, involved agents and objects, with the spatio-temporal component of events being a key feature for contextual analysis. This unveils an enormous potential for exploiting new information sources and opening new research directions. In this paper, we survey the existing literature in this field. We extensively review the employed conceptualization of the notion of event in multimedia, the techniques for event representation and modeling, the feature representation and event inference approaches for the problems of event detection in audio, visual, and textual content. Furthermore, we review some key event-based multimedia applications, and various benchmarking activities that provide solid frameworks for measuring the performance of different event processing and analysis systems. We provide an in-depth discussion of the insights obtained from reviewing the literature and identify future directions and challenges. [ABSTRACT FROM AUTHOR]