In this paper, we introduce the concept of forensic similarity in the speech deepfake detection domain, which aims to determine whether two audio segments share the same underlying forensic traces. Our approach is inspired by prior work in the image domain. To transfer this idea to the audio domain, we propose a two-stage deep learning framework consisting of a Siamese-based feature extractor and a core decision module, referred to as the similarity network. The system goal to assess whether two speech samples originate from the same source by comparing their forensic characteristics. In practice, the model maps pairs of audio segments to a similarity score indicating whether they contain identical or different forensic traces. We evaluate the proposed method on the emerging task of source verification, demonstrating its ability to determine whether two speech samples were generated by the same model. In addition, we explore its applicability to audio splicing detection as a complementary use case. Experimental results show that the proposed approach generalizes well to previously unseen forensic traces, highlighting its robustness, flexibility, and practical relevance for digital audio forensics.

Outdated infrastructure contributes to significant water wastage, where leaks can represent as much as 30% of urban water supply losses. Rapid and precise leak detection is therefore crucial for economic and environmental reasons. Data-driven methods have emerged as promising solutions to detect water leaks due to their accurate performance. However, they encounter obstacles like limited labeled datasets and adapting to various situations. To address these challenges, we explore Semi Supervised Learning (SSL) and Transfer Learning (TL) techniques in the context of water leak detection. We propose to address the problem of leak detection in case of limited labeled data, using a Convolutional Neural Network (CNN) trained on a laboratory-scale network, then adapted to work on real data. To do so, we compare three different domain adaptation techniques that leverage only a small amount of data from the new domain. Our results show that Self-Tuning techniques proves better than the others for this task, even with limited data.

Water is a valuable resource that has to be handled appropriately. However, a significant volume of water is wasted annually due to leaks in Water Distribution Networks (WDNs). This emphasizes the necessity for reliable and effective leak detection and localization systems. Several types of solutions have been proposed during the last few years. Among these solutions, data-driven ones are gaining more traction due to their impressive performance. In this paper, we propose a new method for leak detection and localization. The method is based on water pressure measurements acquired at a series of nodes of a WDN. Our technique is a fully data-driven solution that makes only use of the knowledge of the WDN topology, and a series of pressure data acquisitions obtained in absence of leaks. The proposed solution is based on an autoencoder trained on no-leak data, so that leaks are detected as anomalies. The results achieved on the LeakDB dataset demonstrate that the proposed solution outperforms recent methods for leak detection and localization.

Water is a resource that has to be managed properly. Nevertheless, a sizable amount of water is lost each year because of leaks in Water Distribution Networks (WDNs). The need for trustworthy and efficient leak detection and localization systems is therefore an urgent necessity. For this reason, different solutions have been put out in recent years. Due to their outstanding performance, data-driven methods are among those that are gaining the most popularity. However, the performance of data-driven approaches depend on the coherence between data on which they are trained and data on which they are tested. For example, if the acquired test data look corrupted and incoherent with training ones due to sensor failure, the performance of the overall system may be severely hindered. In this work we present a resilient water leak detection and localization algorithm. It is based on two main steps: the first step analyzes acquired data to possibly recover corrupted ones by means of graph interpolation; the second step finds leaks exploiting an autoencoder-based anomaly detector proposed in the literature. The results show that the suggested approach for signal recovery by means of graph interpolation enables the detector to work in situations in which it would otherwise fail. In doing so, we address a problem that has so far received little attention in the literature: potential sensor failures when acquiring data.

In recent years, the multimedia forensic community has put a great effort in developing solutions to assess the integrity and authenticity of multimedia objects, focusing especially on manipulations applied by means of advanced deep learning techniques. However, in addition to complex forgeries as the deepfakes, very simple yet effective manipulation techniques not involving any use of state-of-the-art editing tools still exist and prove dangerous. This is the case of audio splicing for speech signals, i.e., to concatenate and combine multiple speech segments obtained from different recordings of a person in order to cast a new fake speech. Indeed, by simply adding a few words to an existing speech we can completely alter its meaning. In this work, we address the overlooked problem of detection and localization of audio splicing from different models of acquisition devices. Our goal is to determine whether an audio track under analysis is pristine, or it has been manipulated by splicing one or multiple segments obtained from different device models. Moreover, if a recording is detected as spliced, we identify where the modification has been introduced in the temporal dimension. The proposed method is based on a Convolutional Neural Network (CNN) that extracts model-specific features from the audio recording. After extracting the features, we determine whether there has been a manipulation through a clustering algorithm. Finally, we identify the point where the modification has been introduced through a distance-measuring technique. The proposed method allows to detect and localize multiple splicing points within a recording.