Search

Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it allows for reduced file size while maintaining image quality. Traditional JPEG-based techniques do not apply to HEIF due to its distinct encoding algorithms. We previously proposed a method to detect double compression in HEIF images based on Farid's work on coding ghosts in JPEG images. However, this method was limited to scenarios where the quality parameter used for the first encoding was larger than for the second encoding. In this study, we propose a lightweight image classifier to extend the existing model, enabling the identification of double-compressed images without heavily depending on the input image's quantization history. This extended model outperforms the previous approach and, despite its lightness, demonstrates excellent detection accuracy. [ABSTRACT FROM AUTHOR]

Environmental enrichment (EE) has been shown to produce beneficial effects in addiction disorders; however, due to its configurational complexity, the underlying mechanisms are not yet fully elucidated. Recent evidence suggests that EE, acting as a metaplastic agent, may affect glutamatergic mechanisms underlying appetitive memory and, in turn, modulate reward‐seeking behaviours: here, we have investigated such a possibility following a brief EE exposure. Adult male Sprague–Dawley rats were exposed to EE for 22 h and the expression of critical elements of the glutamate synapse was measured 2 h after the end of EE in the medial prefrontal cortex (mPFC), nucleus accumbens (NAc) and hippocampus (Hipp) brain areas, which are critical for reward and memory. We focused our investigation on the expression of NMDA and AMPA receptor subunits, their scaffolding proteins SAP102 and SAP97, vesicular and membrane glutamate transporters vGluT1 and GLT‐1, and critical structural components such as proteins involved in morphology and function of glutamatergic synapses, PSD95 and Arc/Arg3.1. Our findings demonstrate that a brief EE exposure induces metaplastic changes in glutamatergic mPFC, NAc and Hipp. Such changes are area‐specific and involve postsynaptic NMDA/AMPA receptor subunit composition, as well as changes in the expression of their main scaffolding proteins, thus influencing the retention of such receptors at synaptic sites. Our data indicate that brief EE exposure is sufficient to dynamically modulate the glutamatergic synapses in mPFC‐NAc‐Hipp circuits, which may modulate rewarding and memory processes. [ABSTRACT FROM AUTHOR]

Identifying the originating social network of a digital video is considered a relevant task to support law enforcement agencies and intelligence services in tracing producers of deceptive visual contents. Recent advances in video forensics highlighted how the structure of video containers can be extremely effective in determining the social network of provenance. However, current studies do not consider that a malicious user could easily launder the traces of the social network by rebuilding the container without transcoding. In this letter, we propose a method to identify a video’s originating social network, even when the video container structure is completely unreliable. The proposed method exploits the statistics of DCT coefficients to characterize the different social media encoding properties. With this work, we also built and made available over 1000 videos of different provenance (native, manipulated, exchanged through social networks) to aid the forensic community further researching this topic. [ABSTRACT FROM AUTHOR]

The performance of spread-transform dither modulation (ST-DM) watermarking are evaluated in the presence of two important classes of non additive attacks, such as the gain attack plus noise addition, and the quantization attack .The results reveal that the excellent performance of ST-DM are confirmed in all cases with the only noticeable exception of the gain attack.

Photo Response Non-Uniformity (PRNU) is reputed the most successful trace to identify the source of a digital video. However, its effectiveness is mainly limited by compression and the effect of recently introduced electronic image stabilization on several devices. In the last decade, several approaches were proposed to overcome both these issues, mainly by selecting those video frames which are considered more informative. However, the two problems were always treated separately, and the combined effect of compression and digital stabilization was never considered. This separated analysis makes it hard to understand if achieved conclusions still stand for digitally stabilized videos and if those choices represent a general optimum strategy to perform video source attribution. In this paper, we explore whether an optimum strategy exists in selecting frames based on their type and their positions within the groups of pictures. We, therefore, systematically analyze the PRNU contribute provided by all frames belonging to either digitally stabilized or not stabilized videos. Results on the VISION dataset come up with some insights into optimizing video source attribution in different use cases. [ABSTRACT FROM AUTHOR]

Most video forensic techniques look for traces within the data stream that are, however, mostly ineffective when dealing with strongly compressed or low resolution videos. Recent research highlighted that useful forensic traces are also left in the video container structure, thus offering the opportunity to understand the life-cycle of a video file without looking at the media stream itself. In this article we introduce a container-based method to identify the software used to perform a video manipulation and, in most cases, the operating system of the source device. As opposed to the state of the art, the proposed method is both efficient and effective and can also provide a simple explanation for its decisions. This is achieved by using a decision-tree-based classifier applied to a vectorial representation of the video container structure. We conducted an extensive validation on a dataset of 7000 video files including both software manipulated contents (ffmpeg, Exiftool, Adobe Premiere, Avidemux, and Kdenlive), and videos exchanged through social media platforms (Facebook, TikTok, Weibo and YouTube). This dataset has been made available to the research community. The proposed method achieves an accuracy of 97.6% in distinguishing pristine from tampered videos and classifying the editing software, even when the video is cut without re-encoding or when it is downscaled to the size of a thumbnail. Furthermore, it is capable of correctly identifying the operating system of the source device for most of the tampered videos. [ABSTRACT FROM AUTHOR]

The Variation of Prediction Footprint (VPF), formerly used in video forensics for double compression detection and GOP size estimation, is comprehensively investigated to improve its acquisition capabilities and extend its use to video sequences that contain bi-directional frames (B-frames). By relying on a universal rate-distortion analysis applied to a generic double compression scheme, we first explain the rationale behind the presence of the VPF in double compressed videos and then justify the need of exploiting a new source of information such as the motion vectors, to enhance the VPF acquisition process. Finally, we describe the shifted VPF induced by the presence of B-frames and detail how to compensate the shift to avoid misguided GOP size estimations. The experimental results show that the proposed Generalized VPF (G-VPF) technique outperforms the state of the art, not only in terms of double compression detection and GOP size estimation, but also in reducing computational time. [ABSTRACT FROM AUTHOR]

Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field. [ABSTRACT FROM AUTHOR]

Highlights • Conditioned context is essential for an effective instrumental memory retrieval. • Retrieval inefficacy in a novel context confirms the specificity of our protocol. • Zif268 and rpS6P protein levels correlate to instrumental memory retrieval in rats. Abstract Memory reconsolidation enables the update of a previously consolidated memory trace after its reactivation. Although Pavlovian memory reconsolidation has been widely demonstrated, instrumental memory reconsolidation is still debated. The most critical issue on instrumental memory reconsolidation findings have mainly been linked to the presence of specific boundary conditions for reactivation, for instance contextual parameters. In this study, we investigated the role of the spatial context on molecular markers of sucrose instrumental memory reactivation. Following withdrawal, rats previously conditioned to sucrose self-administration underwent either instrumental memory retrieval or no-retrieval in the conditioned context (Context A, AA condition) or in a modified version of the conditioned context (Context B, AB condition). Two hours later, the level of GluA1 and GluN2B receptors, Zif268 and phosphorylated-rpS6 (rpS6P) was measured in key brain areas for memory reactivation. Retrieval in Context A significantly increased GluA1Rs and GluN2BRs in amygdala compared to no-retrieval, indicating that memory successfully reactivated and destabilized. Moreover, Zif268 level was significantly increased after retrieval in Context A in the nucleus accumbens shell, central and basolateral amygdala but not in the hippocampus, while retrieval in Context B significantly increased Zif268 level in all brain areas. On the other hand, rpS6P level was increased in the nucleus accumbens shell and central amygdala, but decreased in the hippocampus, after retrieval in Context A, while retrieval in Context B did not change rpS6P level in brain areas, except for a small but significant decrease in hippocampus. While the increase of Zif268 level indicated that memory reactivation has been triggered in both the conditions, the lack of change in rpS6P levels after retrieval in Context B – in particular in the central amygdala – suggests that the reconsolidation process could not occur after memory reactivation in a context different from the conditioned one. [ABSTRACT FROM AUTHOR]

Video forensics keeps developing new technologies to verify the authenticity and the integrity of digital videos. While most of the existing methods rely on the analysis of the video data stream, recently, a new line of research was introduced to investigate video life cycle based on the analysis of the video container. Anyway, existing contributions in this field are based on manual comparison of video container structure and content, which is time demanding and error-prone. In this paper, we introduce a method for unsupervised analysis of video file containers, and present two main forensic applications of such method: the first one deals with video integrity verification, based on the dissimilarity between a reference and a query file container; the second one focuses on the identification and classification of the source device brand, based on the analysis of containers structure and content. Noticeably, the latter application relies on the likelihood-ratio framework, which is more and more approved by the forensic community as the appropriate way to exhibit findings in court. We tested and proved the effectiveness of both applications on a dataset composed by 578 videos taken with modern smartphones from major brands and models. The proposed approaches are proved to be valuable also for requiring an extremely small computational cost as opposed to all available techniques based on the video stream analysis or manual inspection of file containers. [ABSTRACT FROM AUTHOR]

Ketamine is a non-competitive antagonist of the NMDA glutamate receptor with psychotomimetic and reinforcing properties, although recent work has pointed out its antidepressant action following acute exposure. Our aim was to investigate the expression of crucial components of the glutamate synapse following chronic ketamine self-administration (S/A), focusing our attention on medial prefrontal cortex (mPFC) and hippocampus (Hip), two brain regions involved in compulsive drug-seeking and drug-related cognitive disorders. Rats self-administered ketamine at a sub-anesthetic dose for 5-6 weeks and were sacrificed 24 h after the last drug exposure. We found a general downregulation of glutamate receptor expression that was brain region-dependent. In fact, in the mPFC, we found reduced expression of NMDA receptor subunits, whereas AMPA receptor protein levels were reduced in Hip; of note, specific scaffolding proteins of NMDA and AMPA receptors were also reduced in mPFC and Hip, respectively. Moreover, the metabotropic mGluR5 receptor was similarly downregulated in these brain regions. These findings reveal a dynamic impairment of glutamate homeostasis in the mPFC and Hip that may represent a signature of long-term exposure to ketamine S/A. Further, this decrement, similarly observed in humans and animal models of schizophrenia may represent a specific feature of the human disease endophenotype. [ABSTRACT FROM AUTHOR]

Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies; moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools. [ABSTRACT FROM AUTHOR]

Wireless communications' infrastructures are frequently selected as a cable replacement in many applications giving an immediate advantage on the wireless investment. However, the worldwide proliferation of wireless local area network (WLAN) imposed large investments on the network security. In the early days of Internet, its layered protocol stack did not consider security as a primary concern. Because then, a significant amount of literature has been published. This paper proposes a watermark-based blind physical layer security utilizing a jamming receiver in conjunction with the spread spectrum watermarking technique. The outage probability of the secrecy capacity is analytically derived, regardless of the eavesdropper position. The theoretical analysis let us draw a secure region around the legitimate receiver. Results indicate how the watermark-based blind physical layer security aims to be a valuable technique for deploying physical layer security. Authors utilized 2 performance metrics, the outage probability of secrecy capacity for assessing the secure communication effectiveness and the error probability for evaluating the watermark extraction process. Finally, the proposed protocol improves the secrecy capacity performance if compared with other protocols, and moreover, it has a lower energy consumption. [ABSTRACT FROM AUTHOR]

Recently, several studies have been concerned with modeling the parenthood relationships between near duplicates in a set of images. Two images share a parenthood relationship if one is obtained by applying transformations to the other. However, this is not the only form of parenting that can exist among images. An image might be a composition created through the combination of the semantic information existent in two or more source images, establishing a relationship between the sources and the composite. The problem of identifying these relations in a set containing near-duplicate subsets of source and composition images is referred to as multiple parenting phylogeny. Thus far, researchers tackled this problem with a three-step solution: 1) separation of near-duplicate groups; 2) classification of the relations between the groups; and 3) identification of the images used to create the original composition. In this work, we extend upon this framework by introducing key improvements, such as better identification of when two images share content, and improved ways to compare this content. In addition, we also introduce a new realistic professionally created data set of compositions involving multiple parenting relationships. The method we present in this paper is properly evaluated through quantitative metrics, established for assessing the accuracy in finding multiple parenting relationships. Finally, we discuss some particularities of the framework, such as the importance of an accurate reconstruction of phylogenies and the method’s behavior when dealing with more complex compositions. [ABSTRACT FROM PUBLISHER]

Image forensic analysis for the detection of contrast enhancement and other histogram-based processing, usually relies on the study of first-order statistics derived from image histogram. Methods based on such an approach, though, are easily circumvented by adopting some counter-forensic attacks. To overcome such a problem, we propose a novel forensic technique based on the study of second-order statistics derived from the co-occurrence matrix. The experiments we carried out demonstrate that the proposed approach is very effective even in the presence of counter-forensic attacks, while it retains the good performance of histogram-based methods when no attack is present. [ABSTRACT FROM AUTHOR]