The broad availability of tools for the acquisition and processing of multimedia signals has recently led to the concern that images and videos cannot be considered a trustworthy evidence, since they can be altered rather easily. This possibility raises the need to verify whether a multimedia content, which can be downloaded from the internet, acquired by a video surveillance system, or received by a digital TV broadcaster, is original or not. To cope with these issues, signal processing experts have been investigating effective video forensic strategies aimed at reconstructing the processing history of the video data under investigation and validating their origins. The key assumption of these techniques is that most alterations are not reversible and leave in the reconstructed signal some “footprints”, which can be analyzed in order to identify the previous processing steps. This paper presents an overview of the video forensic techniques that have been proposed in the literature, focusing on the acquisition, compression, and editing operations, trying to highlight strengths and weaknesses of each solution. It also provides a review of simple processing chains that combine different operations. Anti-forensic techniques are also considered to outline the current limitations and highlight the open research issues.

Video sequences are often believed to provide stronger forensic evidence than still images, e.g., when used in lawsuits. However, a wide set of powerful and easy-to-use video authoring tools is today available to anyone. Therefore, it is possible for an attacker to maliciously forge a video sequence, e.g., by removing or inserting an object in a scene. These forms of manipulation can be performed with different techniques. For example, a portion of the original video may be replaced by either a still image repeated in time or, in more complex cases, by a video sequence. Moreover, the attacker might use as source data either a spatio-temporal region of the same video, or a region taken from an external sequence. In this paper we present the analysis of the footprints left when tampering with a video sequence, and propose a detection algorithm that allows a forensic analyst to reveal video forgeries and localize them in the spatio-temporal domain. With respect to the state-of-the-art, the proposed method is completely unsupervised and proves to be robust to compression. The algorithm is validated against a dataset of forged videos available online.

Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL) solutions thanks to their capability to automatically extract features and achieve high performances. Such evolution has also changed the structure of processing chains and posed new problems to both academic and industrial researchers. The aim of this paper is to provide a comprehensive overview of the latest state-of-the-art DL approaches for the most crucial PC processing operations, i.e., semantic scene understanding, compression, and completion. With respect to the existing reviews, the work proposes a new taxonomical classification of the approaches, taking into account the characteristics of the acquisition set up, the peculiarities of the acquired PC data, the presence of side information (depending on the adopted dataset), the data formatting, and the characteristics of the DL architectures. This organization allows one to better comprehend some final performance comparisons on common test sets and cast a light on the future research trends.

The analysis of double-compressed images is a problem largely studied by the multimedia forensics community, as it might be exploited, e.g., for tampering localization or source device identification. In many practical scenarios, e.g. photos uploaded on blogs, on-line albums, and photo sharing Web sites, images might be compressed several times. However, the identification of the number of compression stages applied to an image remains an open issue. This paper proposes a forensic method based on the analysis of the distribution of the first significant digits of DCT coefficients, which is modeled according to Benford's law. The method relies on a set of Support Vector Machine (SVM) classifiers and allows us to accurately identify the number of compression stages applied to an image. Up to four consecutive compression stages were considered in the experimental validation. The proposed approach extends and outperforms the previously published methods aimed at detecting double JPEG compression.

Video content is routinely acquired and distributed in digital format. Therefore, it is customary to have the content encoded multiple times. In this paper we consider a processing chain of two coding steps and we propose a method that aims at identifying the type of codec used in the first step, by analyzing its coding-based footprints. The method relies on the fact that lossy coding is an almost idempotent operation, i.e., re-encoding the reconstructed sequence with the same codec and coding parameters produces a sequence that is highly correlated with the input one. As a consequence, it is possible to analyze this sort of correlation to identify the first codec provided that the second codec does not introduce severe quality degradation. The proposed solution finds several applications in the field of multi-media forensics, e.g. to identify the device that generated the original video stream or detect collages of different sequences.

Nowadays, thanks to the increasingly availability of powerful processors and user friendly applications, the editing of video sequences is becoming more and more frequent. Moreover, after each editing step, any video object is almost always encoded in order to store it using a less amount of memory. For this reason, inferring the number of compression steps that have been applied to such a multimedia object is an important clue in order to assess its authenticity. In this paper we propose a method to recover the number of compression steps applied to a video sequence. In order to accomplish this goal, we make use of a classifier based on multiple Support Vector Machines (SVM) exploiting the Benford's law. Indeed, the feature vectors used to train and test the SVM are based on the statistics of the most significant digit of quantized transform coefficients. The proposed method is tested with a generic hybrid video encoder combining motion-compensation and block coding. Results show that this method is able to discriminate up to three compression stages with high accuracy.