Audio Splicing Detection and Localization Based on Acquisition Device Traces

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

Meniscoplasty for stable osteochondritis dissecans of the lateral femoral condyle combined with a discoid lateral meniscus: a case report

<p>Abstract</p> <p>Introduction</p> <p>Osteochondritis dissecans of the lateral femoral condyle is relatively rare, and it is reported to often be combined with a discoid lateral meniscus. Given the potential for healing, conservative management is indicated for stable osteochondritis dissecans in patients who are skeletally immature. However, patients with osteochondritis dissecans of the lateral femoral condyle combined with a discoid lateral meniscus often have persistent symptoms despite conservative management.</p> <p>Case presentation</p> <p>We present the case of a seven-year-old Korean girl who had osteochondritis dissecans of the lateral femoral condyle combined with a discoid lateral meniscus, which healed after meniscoplasty for the symptomatic lateral discoid meniscus without surgical intervention for the osteochondritis dissecans. In addition, healing of the osteochondritis dissecans lesion was confirmed by an MRI scan five months after the operation.</p> <p>Conclusions</p> <p>Meniscoplasty can be recommended for symptomatic stable juvenile osteochondritis dissecans of the lateral femoral condyle combined with a discoid lateral meniscus when conservative treatment fails.</p

Detection and localization of partial audio matches

Within recent years, several applications have emerged which require detection and accurate localization of unknown partial audio matches within a dataset. This requirement cannot be adequately addressed with state-of-the-art matching approaches based on fingerprinting. We propose a new approach that supports partial matching detection and localization within a dataset, and we evaluate it against a popular audio matching algorithm, showing that it performs significantly better for the given problem domain

Spectral Denoising for Microphone Classification

In this paper, we propose the use of denoising for microphone classification, to enable its usage for several key application domains that involve noisy conditions. We describe the proposed analysis pipeline and the baseline algorithm for microphone classification, and discuss various denoising approaches which can be applied to it within the time or spectral domain; finally, we determine the best-performing denoising procedure, and evaluate the performance of the overall, integrated approach with several SNR levels of additive input noise. As a result, the proposed method achieves an average accuracy increase of about 25% on denoised content over the reference baseline

Speaker-Independent Microphone Identification in Noisy Conditions

This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings