Audio deepfakes represent a growing threat to digital security and trust, leveraging advanced generative models to produce synthetic speech that closely mimics real human voices. Detecting such manipulations is especially challenging under open-world conditions, where spoofing methods encountered during testing may differ from those seen during training. In this work, we propose an end-to-end deep learning framework for audio deepfake detection that operates directly on raw waveforms. Our model, RawNetLite, is a lightweight convolutional-recurrent architecture designed to capture both spectral and temporal features without handcrafted preprocessing. To enhance robustness, we introduce a training strategy that combines data from multiple domains and adopts Focal Loss to emphasize difficult or ambiguous samples. We further demonstrate that incorporating codec-based manipulations and applying waveform-level audio augmentations (e.g., pitch shifting, noise, and time stretching) leads to significant generalization improvements under realistic acoustic conditions. The proposed model achieves over 99.7% F1 and 0.25% EER on in-domain data (FakeOrReal), and up to 83.4% F1 with 16.4% EER on a challenging out-of-distribution test set (AVSpoof2021 + CodecFake). These findings highlight the importance of diverse training data, tailored objective functions and audio augmentations in building resilient and generalizable audio forgery detectors. Code and pretrained models are available at https://iplab.dmi.unict.it/mfs/Deepfakes/PaperRawNet2025/.

In this companion paper, we reproduce the experiments presented in our work titled ''NIF: A Fast Implicit Image Compression with Bottleneck Layers and Modulated Sinusoidal Activations'' [2], presented at ACM Multimedia 2023. In this study, we present the architecture and the technical details of our implementation and provide instructions to reproduce the main results, the ablation study, the plots and the figures presented in the paper. All the material described in this paper is released on GitHub [3], featuring the full results, a reference software implementation and a generic environment setup that works on any system, even without a GPU.

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.

Implicit Neural Representations (INR) are a novel data representation technique which is gaining ground in the image compression field due to its simplicity and interesting results in terms of rate/distortion ratio. Although a variety of methods based on this paradigm were proposed, limited interest has been given to the analysis of the loss function and the impact of compression artifacts on the visual quality of the reconstructed images, which are mainly due to the adoption of the simple Mean Squared Error (MSE) loss function and to the evaluation done merely in terms of Peak Signal-to-Noise Ratio (PSNR), which do not often correlate with the human perception. In this paper, we evaluate a set of five loss functions in the context of training INRs for image compression, applied to three state-of-the-art architectures, and evaluate their effect on a broader collection of quantitative metrics and the visual fidelity of the decoded images to the originals. The presented outcomes show that the reconstructions obtained by training with some loss functions as MSE suffer from over-smoothing and aliasing artifacts. Our findings reveal that through the employing of a suitable loss function, state-of-the-art architectures quantitatively and qualitatively outperform the results reported in their original papers.

In Implicit Neural Representations (INRs) a discrete signal is parameterized by a neural network that maps coordinates to the signal samples. INRs were successfully employed for encoding and compression, but such approaches are in their early stage and are still overcome by traditional codecs and autoencoders. Despite this, they have recently gained the attention of the research community due to their promising results as novel representation strategies for encoding visual content. In this paper, we propose Neural Imaging Format (NIF), an open-source INR-based image compression codec which takes advantage of a novel neural architecture which consists of two modules: a Genesis network, for mapping coordinates to pixels through bottleneck layers with sinusoidal activation units, and a Modulation network, for varying the period of the sinusoidal activations. Additionally, a final weights quantization step leads to an improvement in the compression ratio. Our proposal (NIF) consistently outperforms state-of-art INR-based compressors in terms of PSNR, by achieving comparable or better results with an outstanding up to X26 encoding speed. We also show that NIF reduces the gap between INR-based methods with respect to traditional approaches. Interestingly, our approach outperforms established codecs such as JPEG and WebP when one encodes high-resolution images at low-bitrate regimes. Extensive experiments on different datasets, a visual comparison, and an ablation study, prove the validity of the proposed approach.

This abstract provides a summary and overview of the 8th International Workshop on Multimedia Assisted Dietary Management.

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.

This abstract provides a summary and overview of the 7th International Workshop on Multimedia Assisted Dietary Management.

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.

Colonoscopy is currently the most effective screening method to find precancerous colon polyps and plan their removal. Computer-aided polyp detection can reduce polyp miss detection rates and help doctors find the most critical regions to pay attention to. The challenge in detecting polyps is due to the polyp's morphology and size, and these fall into false-negative. Indeed, polyps may exhibit high variability in shapes (e.g., depressed, flat, pedunculated, etc ...). Moreover, the water injected from the endoscope results in artifacts which impede the detection, and the lubricating mucus causes light artifacts due its glossiness. To address this problem, we propose a mask-based attention mechanism to ensure that the employed detector focuses on particular regions of the image in order to reduce misdetection rate. Our contribution takes advantage of information on polyp's position over time within a video sequence. We provide such information through a binary mask which points out the last-known polyp's position. The proposed approach is validated on a dataset that has been labeled by colonoscopy experts. It contains about 200 videos and more than 500 different polyps with high variability in size and textures. Experimental results show that the proposed attention mechanism recover a smaller number of false negatives and achieves an Fl-score of 80.21%.

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.

The manual review of raw surveillance video is a time consuming task which can be optimized by using a Video Synopsis (VS) algorithm. The aim of such approaches is to condense a long video into shorter one to allow a quicker review of surveillance data. However, VS is a complex problem. A typical object-based VS algorithm requires three main modules to perform the following tasks: object detection and tracking, tubes rearrangement, condensed video generation. Although the aforementioned three steps are equally critical, we realized that the core of Video Synopsis lies in the tubes rearrangement. This led us to propose an original approach to tackle the problem of tubes rearrangement. To this aim, we first introduce a new toolbox to generate a proper testing dataset, which allows to bypass the lack of public databases including proper annotated videos for testing synopsis approaches. Additionally, we propose an improvement of a tubes arrangement algorithm based on graph colouring and we prove its validity on our generated dataset. For a proper comparison, we show that our algorithm also outperforms the original one on UA-DETRAC public dataset.

This abstract provides a summary and overview of the 5th International Workshop on Multimedia Assisted Dietary Management.

Healthcare system quality improvements depend both on the availability of innovative technologies and on proper investments to transfer experimental policies into daily practices that could be easily adopted in all hospitals. Unfortunately, funds are generally not enough to cover all the addressable issues and the policy makers are faced with the difficult problem to decide where to allocate the money to produce the most relevant positive outcomes. To support this decision process, data gathering, and analysis play a key role. In this contribution we propose a simplified pipeline that starting from observational data to achieve statistical conclusions as valid as in designed randomized studies. After detailing the proposed analytic method, its soundness is proved using an important case study: the problem of the reduction of Healthcare-Associated Infections, and especially those acquired in Intensive Care Units. In particular, we show how to estimate the preventable proportion of Intubation-Associated Pneumonia in ICUs. In our study, using G-Computation based approach, we found out that the preventable proportion for IAP is of 44%. Interestingly, when bundle compliance is added in the statistical model, the preventable proportion for IAP is of 40%.

This paper presents the software architecture of a robotic museum guide application called Cuma. It is intended to run upon the Pepper robotic platform and has the objective of guiding visitors of a museum accompanying them in the tour, explaining museum works, and interacting with them in order to gather feedback. Cuma has been partially implemented and preliminarily tested. The results reported in the paper, highlight that even if Pepper, from the structural point of view, seems particularly suited for this kind of application, the provided software platform presents some important limitations thus requiring the integration of external tools and algorithms.

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.