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In this paper, we investigate the counter-forensic effects of the forthcoming JPEG AI standard based on neural image compression, focusing on two critical areas: deepfake image detection and image splicing localization. Neural image compression leverages advanced neural network algorithms to achieve higher compression rates while maintaining image quality. However, it introduces artifacts that closely resemble those generated by image synthesis techniques and image splicing pipelines, complicating the work of researchers when discriminating pristine from manipulated content. We comprehensively analyze JPEG AI's counter-forensic effects through extensive experiments on several state-of-the-art detectors and datasets. Our results demonstrate that an increase in false alarms impairs the performance of leading forensic detectors when analyzing genuine content processed through JPEG AI. By exposing the vulnerabilities of the available forensic tools we aim to raise the urgent need for multimedia forensics researchers to include JPEG AI images in their experimental setups and develop robust forensic techniques to distinguish between neural compression artifacts and actual manipulations.

Designing well-defined magnetic nanomaterials is crucial for various applications and it demands a comprehensive understanding of their magnetic properties at the microscopic level. In this study, we investigate the contributions to the total anisotropy of Mn-Co mixed spinel nanoparticles. By employing neutron measurements sensitive to the spatially resolved surface anisotropy with sub-\AA\space resolution, we reveal an additional contribution to the anisotropy constant arising from shape anisotropy and interparticle interactions. Our findings shed light on the intricate interplay between chemical composition, microstructure, morphology, and surface effects, providing valuable insights for the design of advanced magnetic nanomaterials for AC biomedical applications, such as cancer treatment by magnetic fluid hyperthermia., Comment: main manuscript 18 pages, 4 figures, supplemental: 9 pages, 4 figures

The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. This widespread use and easy availability have unfortunately made SAR data susceptible to malicious alterations, such as local editing applied to the images for inserting or covering the presence of sensitive targets. Vulnerability is further emphasized by the fact that most SAR products, despite their original complex nature, are often released as amplitude-only information, allowing even inexperienced attackers to edit and easily alter the pixel content. To contrast malicious manipulations, in the last years the forensic community has begun to dig into the SAR manipulation issue, proposing detectors that effectively localize the tampering traces in amplitude images. Nonetheless, in this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image. We refer to this approach as a counter-forensic attack. To achieve the concealment of manipulation traces, the attacker can simulate a re-acquisition of the manipulated scene by the SAR system that initially generated the pristine image. In doing so, the attacker can obscure any evidence of manipulation, making it appear as if the image was legitimately produced by the system. We assess the effectiveness of the proposed counter-forensic approach across diverse scenarios, examining various manipulation operations. The obtained results indicate that our devised attack successfully eliminates traces of manipulation, deceiving even the most advanced forensic detectors.

We present a mean field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyse the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units $f< f_t \leq 1/2$, even for a finite system. In finite systems, when $f > f_t$ the first order transition is replaced by a pseudo critical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behaviour of a continuous phase transition order parameter. However, in the thermodynamic limit, we found that $f_t\to 1/2$ and the activity for $f\geq f_t$ becomes negligible for any value of $T>0$, while the first order transition for $f

The integration of two-dimensional $MoS_{2}$ with $GaN$ recently attracted significant interest for future electronic/optoelectronic applications. However, the reported studies have been mainly carried out using heteroepitaxial $GaN$ templates on sapphire substrates, whereas the growth of $MoS_{2}$ on low-dislocation-density bulk GaN can be strategic for the realization of truly vertical devices. In this paper, we report the growth of ultrathin $MoS_{2}$ films, mostly composed by single-layers ($1L$), onto homoepitaxial $n-GaN$ on $n^{+}$ bulk substrates by sulfurization of a pre-deposited $MoO_{x}$ film. Highly uniform and conformal coverage of the $GaN$ surface was demonstrated by atomic force microscopy, while very low tensile strain (0.05%) and a significant $p^{+}$-type doping ($4.5 \times 10^{12} cm^{-2}$) of $1L-MoS_{2}$ was evaluated by Raman mapping. Atomic resolution structural and compositional analyses by aberration-corrected electron microscopy revealed a nearly-ideal van der Waals interface between $MoS_{2}$ and the $Ga$-terminated $GaN$ crystal, where only the topmost $Ga$ atoms are affected by oxidation. Furthermore, the relevant lattice parameters of the $MoS_{2}/GaN$ heterojunction, such as the van der Waals gap, were measured with high precision. Finally, the vertical current injection across this 2D/3D heterojunction has been investigated by nanoscale current-voltage analyses performed by conductive atomic force microscopy, showing a rectifying behavior with an average turn-on voltage $V_{on}=1.7 V$ under forward bias, consistent with the expected band alignment at the interface between $p^{+}$ doped $1L-MoS_{2}$ and $n-GaN$., Comment: 21 pages, 6 figures

Due to the reduction of technological costs and the increase of satellites launches, satellite images are becoming more popular and easier to obtain. Besides serving benevolent purposes, satellite data can also be used for malicious reasons such as misinformation. As a matter of fact, satellite images can be easily manipulated relying on general image editing tools. Moreover, with the surge of Deep Neural Networks (DNNs) that can generate realistic synthetic imagery belonging to various domains, additional threats related to the diffusion of synthetically generated satellite images are emerging. In this paper, we review the State of the Art (SOTA) on the generation and manipulation of satellite images. In particular, we focus on both the generation of synthetic satellite imagery from scratch, and the semantic manipulation of satellite images by means of image-transfer technologies, including the transformation of images obtained from one type of sensor to another one. We also describe forensic detection techniques that have been researched so far to classify and detect synthetic image forgeries. While we focus mostly on forensic techniques explicitly tailored to the detection of AI-generated synthetic contents, we also review some methods designed for general splicing detection, which can in principle also be used to spot AI manipulate images, Comment: 25 pages, 17 figures, 5 tables, APSIPA 2022

While the support for the relevance of critical dynamics to brain function is increasing, there is much less agreement on the exact nature of the advocated critical point. Thus, a considerable number of theoretical efforts are currently concentrated on which mechanisms and what type/s of transition can be exhibited by neuronal networks models. In that direction, the present work describes the effect of incorporating a fraction of inhibitory neurons on the collective dynamics. As we show, this results in the appearence of a tricritical point for highly connected networks and non-zero fraction of inhibitory neurons. We discuss the relation of the present results with relevant experimental evidence., Comment: 7 pages, 7 figures

The advent of novel opto-genetics technology allows the recording of brain activity with a resolution never seen before. The characterisation of these very large data sets offers new challenges as well as unique theory-testing opportunities. Here we discuss whether the spatial and temporal correlation of the collective activity of thousands of neurons are tangled as predicted by the theory of critical phenomena. The analysis shows that both, the correlation length $\xi$ and the correlation time $\tau$ scale as predicted as a function of the system size. With some peculiarities that we discuss, the analysis uncovers new evidence consistent with the view that the large scale brain cortical dynamics corresponds to critical phenomena., Comment: 8 pages, 6 figures

In this article, a correlation metric $\kappa_C$ is proposed for the inference of the dynamical state of neuronal networks. $\kappa_C$ is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implementation is first studied on a neuronal network model, where the results of this new metric coincide with those obtained from neuronal avalanche analysis, thus well characterizing the critical state of the network. The approach is further tested with brain optogenetic recordings in behaving mice from a publicly available database. Potential applications and limitations for its use with currently available optical imaging techniques are discussed., Comment: 8 pages, 8 figures

Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integrity assessment of SAR images was never investigated. This task poses new challenges, since SAR images are generated with a processing chain completely different from that of natural photographs. This implies that many forensics methods developed for natural images are not guaranteed to succeed. In this paper, we investigate the problem of amplitude SAR imagery splicing localization. Our goal is to localize regions of an amplitude SAR image that have been copied and pasted from another image, possibly undergoing some kind of editing in the process. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images., Comment: The manuscript has been published in IEEE Access. Changes include the full citation to the IEEE published version

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images. In this paper, we focus on the detection of synthetically generated western blot images. These images are largely explored in the biomedical literature and it has been already shown they can be easily counterfeited with few hopes to spot manipulations by visual inspection or by using standard forensics detectors. To overcome the absence of publicly available data for this task, we create a new dataset comprising more than 14K original western blot images and 24K synthetic western blot images, generated using four different state-of-the-art generation methods. We investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors. In both scenarios, we never exploit synthetic western blot images at training stage. The achieved results show that synthetically generated western blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images. We also test the robustness of the developed detectors against post-processing operations commonly performed on scientific images, showing that we can be robust to JPEG compression and that some generative models are easily recognizable, despite the application of editing might alter the artifacts they leave.

This report is concerned with the relevance of the microscopic rules, that implement individual neuronal activation, in determining the collective dynamics, under variations of the network topology. To fix ideas we study the dynamics of two cellular automaton models, commonly used, rather in-distinctively, as the building blocks of large scale neuronal networks. One model, due to Greenberg \& Hastings, (GH) can be described by evolution equations mimicking an integrate-and-fire process, while the other model, due to Kinouchi \& Copelli, (KC) represents an abstract branching process, where a single active neuron activates a given number of postsynaptic neurons according to a prescribed "activity" branching ratio. Despite the apparent similarity between the local neuronal dynamics of the two models, it is shown that they exhibit very different collective dynamics as a function of the network topology. The GH model shows qualitatively different dynamical regimes as the network topology is varied, including transients to a ground (inactive) state, continuous and discontinuous dynamical phase transitions. In contrast, the KC model only exhibits a continuous phase transition, independently of the network topology. These results highlight the importance of paying attention to the microscopic rules chosen to model the inter-neuronal interactions in large scale numerical simulations, in particular when the network topology is far from a mean field description. One such case is the extensive work being done in the context of the Human Connectome, where a wide variety of types of models are being used to understand the brain collective dynamics., Comment: 11 pages, 9 figures (including Supplemental Material)

The gold-assisted exfoliation is a very effective method to produce large-area ($cm^2$-scale) membranes of molybdenum disulfide ($MoS_2$) for electronics. However, the strong $MoS_2/Au$ interaction, beneficial for the exfoliation process, has a strong impact on the vibrational and light emission properties of $MoS_2$. Here, we report an atomic force microscopy (AFM), micro-Raman ($\mu-R$) and micro-Photoluminescence ($\mu-PL$) investigation of $MoS_2$ with variable thickness exfoliated on Au and subsequently transferred on an $Al_2O_3/Si$ substrate. The $E_{2g}$ - $A_{1g}$ vibrational modes separation $\Delta\mu$ (typically used to estimate $MoS_2$ thickness) exhibits an anomalous large value ($\Delta\mu=21.2 cm^{-1}$) for monolayer (1L) $MoS_2$ on Au as compared to the typical one ($\Delta\mu=18.5 cm^{-1}$) measured on 1L $MoS_2$ on $Al_2O_3/Si$. Such substrate-related differences, explained in terms of tensile strain and p-type doping arising from the $MoS_2/Au$ interaction, were found to gradually decrease while increasing the number of $MoS_2$ layers. Furthermore, $\mu-PL$ spectra for 1L $MoS_2$ on Au exhibit a strong quenching and an overall red-shift of the main emission peak at 1.79 eV, compared to the 1.84 eV peak for 1L $MoS_2$ on $Al_2O_3$. After PL spectra deconvolution, such red shift was explained in terms of a higher trion/exciton intensity ratio, probably due to the higher polarizability of the metal substrate, as well as to the smaller equilibrium distance at $MoS_2/Au$ interface., Comment: 14 pages, 4 figures

In this paper we demonstrated the thermal Atomic Layer Deposition (ALD) growth at 250 {\deg}C of highly homogeneous and ultra-thin ($\approx$ 3.6 nm) $Al_2O_3$ films with excellent insulating properties directly onto a monolayer (1L) $MoS_2$ membrane exfoliated on gold. Differently than in the case of 1L $MoS_2$ supported by a common insulating substrate ($Al_2O_3/Si$), a better nucleation process of the high-k film was observed on the 1L $MoS_2/Au$ system since the ALD early stages. Atomic force microscopy analyses showed a $\approx 50\%$ $Al_2O_3$ surface coverage just after 10 ALD cycles, its increasing up to $>90\%$ (after 40 cycles), and an uniform $\approx$ 3.6 nm film, after 80 cycles. The coverage percentage was found to be significantly reduced in the case of 2L $MoS_2/Au$, indicating a crucial role of the interfacial interaction between the aluminum precursor and $MoS_2/Au$ surface. Finally, Raman spectroscopy and PL analyses provided an insight about the role played by the tensile strain and p-type doping of 1L $MoS_2$ induced by the gold substrate on the enhanced high-k nucleation of $Al_2O_3$ thin films. The presently shown high quality ALD growth of high-k $Al_2O_3$ dielectrics on large area 1L $MoS_2$ induced by the Au underlayer can be considered of wide interest for potential device applications based on this material system., Comment: 21 pages, 6 figures

Gold-assisted mechanical exfoliation currently represents a promising method to separate ultra-large (cm-scale) transition metal dichalcogenides (TMDs) monolayers (1L) with excellent electronic and optical properties from the parent van der Waals (vdW) crystals. The strong interaction between $Au$ and chalcogen atoms is the key to achieve this nearly perfect 1L exfoliation yield. On the other hand, it may affect significantly the doping and strain of 1L TMDs in contact with Au. In this paper, we systematically investigated the morphology, strain, doping, and electrical properties of large area 1L $MoS_{2}$ exfoliated on ultra-flat $Au$ films ($0.16-0.21 nm$ roughness) and finally transferred to an insulating $Al_{2}O_{3}$ substrate. Raman mapping and correlative analysis of the $E'$ and $A1'$ peaks positions revealed a moderate tensile strain ($0.2\%$) and p-type doping ($n=-0.25 \times 10^{13} cm^{-2}$) of 1L $MoS_{2}$ in contact with $Au$. Nanoscale resolution current mapping and current-voltage (I-V) measurements by conductive atomic force microscopy (C-AFM) showed direct tunnelling across the 1L $MoS_{2}$ on $Au$, with a broad distribution of tunnelling barrier values (from 0.7 to 1.7 eV) consistent with the p-type doping of $MoS_{2}$. After the final transfer of $1L-MoS_{2}$ on $Al_{2}O_{3}/Si$, the strain was converted to compressive ($-0.25\%$). Furthermore, an n-type doping ($n=0.5 \times 10^{13} cm^{-2}$) was deduced by Raman mapping and confirmed by electrical measurements of an $Al_{2}O_{3}/Si$ back-gated 1L $MoS_{2}$ transistor. These results provide a deeper understanding of the $Au$-assisted exfoliation mechanisms and can contribute to its widespread applications for the realization of novel devices and artificial vdW heterostructures