The growth of Artificial Intelligence applications based on Natural Language Processing requires to develop risk management models that can balance opportunities with risks, especially in high-stakes scenarios. In this paper, we contribute to the development of risk models presenting two integrated statistical metrics that can measure the "Accuracy" and the "Sustainability" of Artificial Intelligence models based on text processing, in line with the requests of international recommendations and regulations, such as the European Artificial Intelligence Act. The framework is validated through experiments on three distinct binary classification tasks on widely differing data sources. By highlighting vulnerabilities and strengths across diverse NLP pipelines, the proposed metrics provide a practical tool for assessing the reliability of AI applications. These contributions aim to foster safer deployment of AI technologies in finance, by mitigating risks of potential harms to financial stability.

Abstract Digital games often play the role of vectors for innovations in computer graphics and multimedia. Contemporary digital games feature realistic graphics and complex mechanics, which increase the computational burden of a proper gaming experience; consequently, the costs for customer equipment raises. Cloud gaming is a technique in which a high-performance server running a videogame receives the player’s input and streams the video back to a lightweight client. Currently, available open-source frameworks designed in this scope suffer from deprecation, limited capabilities or strict hardware and operating system dependencies. In this paper, we present a novel open-source framework to define and benchmark architectures for remote rendering of screen content, to provide a platform-agnostic tool for researchers that want to contribute to this area. We run a substantial set of experiments about streaming gaming sessions with different combinations of network conditions, codec parameters and transmission policies, thus collecting network statistics and dumping transmitted and received video frames, finally demonstrating the usability of the provided tools. We show the various features of the framework to profile and perform both an analysis of network statistics and of the stream’s visual quality.

Detecting and recognizing deepfakes is a pressing issue in the digital age. In this study, we first collected a dataset of pristine images and fake ones properly generated by nine different Generative Adversarial Network (GAN) architectures and four Diffusion Models (DM). The dataset contained a total of 83,000 images, with equal distribution between the real and deepfake data. Then, to address different deepfake detection and recognition tasks, we proposed a hierarchical multi-level approach. At the first level, we classified real images from AI-generated ones. At the second level, we distinguished between images generated by GANs and DMs. At the third level (composed of two additional sub-levels), we recognized the specific GAN and DM architectures used to generate the synthetic data. Experimental results demonstrated that our approach achieved more than 97% classification accuracy, outperforming existing state-of-the-art methods. The models obtained in the different levels turn out to be robust to various attacks such as JPEG compression (with different quality factor values) and resize (and others), demonstrating that the framework can be used and applied in real-world contexts (such as the analysis of multimedia data shared in the various social platforms) for support even in forensic investigations to counter the illicit use of these powerful and modern generative models. We are able to identify the specific GAN and DM architecture used to generate the image, which is critical in tracking down the source of the deepfake. Our hierarchical multi-level approach to deepfake detection and recognition shows promising results in identifying deepfakes allowing focus on underlying task by improving (about 2% on the average) standard multiclass flat detection systems. The proposed method has the potential to enhance the performance of deepfake detection systems, aid in the fight against the spread of fake images, and safeguard the authenticity of digital media.

Accurate segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) scans is crucial for clinical diagnosis and effective treatment planning. In this work, we investigate the effectiveness of Diffusion Models (DM) in achieving pixel-wise segmentation of MS lesions. DM significantly improves segmentation sensitivity, especially in regions with subtle abnormalities. We conducted extensive experiments using the magnetic resonance volumes from a public dataset, encompassing various imaging modalities. Our analysis demonstrated how DM can achieve performance levels that are on par with state-of-the-art techniques, as evidenced by a mean Dice coefficient comparable to the best existing methods. Furthermore, some variants of standard DM exhibits robustness across various imaging modalities, showcasing its versatility in clinical settings.

Abstract A crime scene can provide valuable evidence critical to explain reason and modality of the occurred crime, and it can also lead to the arrest of criminals. The type of evidence collected by crime scene investigators or by law enforcement may accordingly effective involved cases. Bullets and cartridge cases examination is of paramount importance in forensic science because they may contain traces of microscopic striations, impressions and markings, which are unique and reproducible as “ballistic fingerprints”. The analysis of bullets and cartridge cases is a complicated and challenging process, typically based on optical comparison, leading to the identification of the employed firearm. New methods have recently been proposed for more accurate comparisons, which rely on three-dimensionally reconstructed data. This paper aims at further advancing recent methods by introducing a novel immersive technique for ballistics comparison by means of Virtual Reality. Users can three-dimensionally examine the cartridge cases shapes through intuitive natural gestures, from any vantage viewpoint (including internal iper-magnified views), while having at their disposal sets of visual aids which could not be easily implemented in desktop-based applications. A user study was conducted to assess viability and performance of our solution, which involved fourteen individuals acquainted with the standard procedures used by law enforcement agencies. Results clearly indicated that our approach lead to faster adaptation of users to the UI/UX and more accurate and explainable ballistics examination results.

Undetected gas leakages may result in serious fire and explosion accidents with consequences like injuries among workers and financial losses. Automated leak detectors aimed to catch in time the gas emissions could reduce the incident risks. Several monitoring techniques have been developed over the years, among them the Optical Gas Imaging (OGI) is a widely-used method but it typically requires manual analysis (slow and error-prone). This paper introduces an automated gas leakage detection framework exploiting Infrared video data. A novel Recurrent Neural Network architecture was designed and trained on an ad-hoc collected large-scale dataset. Experimental results demonstrated the effectiveness of the proposed framework outperforming the state-of-the-art approaches with an average accuracy of 98%. The robustness of the technique was also validated in different scenarios and with different camera settings.

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions.

Abstract The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights.

After thirty years of the GIF file format, today is becoming more popular than ever: being a great way of communication for friends and communities on Instant Messengers and Social Networks. While being so popular, the original compression method to encode GIF images have not changed a bit. On the other hand popularity means that storage saving becomes an issue for hosting platforms. In this paper a parametric optimization technique for animated GIFs will be presented. The proposed technique is based on Local Color Table selection and color remapping in order to create optimized animated GIFs while preserving the original format. The technique achieves good results in terms of byte reduction with limited or no loss of perceived color quality. Tests carried out on 1000 GIF files demonstrate the effectiveness of the proposed optimization strategy.

Fingerprints are one of the most copious evidence in a crime scene and, for this reason, they are frequently used by law enforcement for identification of individuals. But fingerprints can be altered. “Altered fingerprints” refers to intentionally damage of the friction ridge pattern and they are often used by smart criminals in hope to evade law enforcement. We use a deep neural network approach training an Inception-v3 architecture. This paper proposes a method for detection of altered fingerprints, identification of types of alterations and recognition of gender, hand and fingers. We also produce activation maps that show which part of a fingerprint the neural network has focused on, in order to detect where alterations are positioned. The proposed approach achieves an accuracy of 98.21%, 98.46%, 92.52%, 97.53% and 92,18% for the classification of fakeness, alterations, gender, hand and fingers, respectively on the SO.CO.FING. dataset.

Multimedia Forensics experts work consists in providing answers about integrity of a specific media content and from where it comes from. Exploitation of any traces from JPEG double compressed images is often one of the main investigative path to be used for these purposes. Thus it is fundamental to have tools and algorithms able to safely estimate the first quantization matrix to further proceed with camera model identification and related tasks. In this paper, a technique based on extensive simulation is proposed, with the aim to infer the first quantization for a certain numbers of Discrete Cosine Transform (DCT) coefficients exploiting local image statistics without using any a-priori knowledge. The method provides also a reliable confidence value for the estimation which is of great importance for forensic purposes. Experimental results w.r.t. the state-of-the-art demonstrate the effectiveness of the proposed technique both in terms of precision and overall reliability.

Firearm identification is crucial in many investigative scenario. The crime scene often contains traces left by firearms in terms of bullets and cartridges. Traces analysis is a fundamental step in the Forensics Ballistics Analysis Process to identify which firearm fired a specific cartridge. In this paper we present a fully automated technique to compare cartridges represented as a set of 3D point-clouds. The overall approach is based on Siamese Neural Network learning paradigm that we use to build a suitable embedding space where the 3D point-cloud of the cartridges are compared. The proposed approach has been assessed by considering the NBTRD dataset. Obtained results support the exploitation of the proposed technique in ballistic analysis.

The authentication of printed material based on textures is a critical and challenging problem for many security agencies in many contexts: valuable documents, banknotes, tickets or rare collectible cards are often targets for forgery. This motivates the study of low-cost, fast and reliable approaches for documents authenticity analysis. In this paper, we present a new approach based on the extraction of translucent patterns from paper sheet by means of a specific-built framework. A fingerprint is obtained by computing a Local Binary Pattern descriptor on the digital image. To validate the robustness of the proposed method for authentication analysis, we introduce a novel dataset and perform retrieval tests under both, ideal and noisy conditions. Experimental results prove the validity of the proposed strategy.

Color re-indexing is one of main approaches for improving the loss-less compression of color indexed images. Zero-order entropy reduction of indexes matrix is the key to obtain high compression ratio. However, obtaining the optimal re-indexed palette is a challenging problem that cannot be solved by brute-force approaches. In this paper we propose a novel re-indexing approach where the Travelling Salesman Problem is solved through Ant Colony Optimization. Our method is proved to achieve high quality results by outperforming state-of-art ones in term of compression gain. Additionally, we exploit clustering and GPU computing to make our solution extremely fast.