A Quantitative Evaluation Framework of Video De-Identification Methods

Buršić, Sathya; D’Amelio, Alessandro; Granato, Marco; Grossi, Giuliano; Lanzarotti, Raffaella

doi:10.1109/icpr48806.2021.9412186

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…Such K user-created video and image content might convey any sort of message (verbally, non-verbally, textually or by other visual means) which can be the source of a range of threats on social media. Concrete examples are the propagation of beauty stereotypes via image data [125] or hyper-realistic videos/images showing people saying and doing things that never happened [25,132], so called "deep fakes". Fig.…”

Section: Content-based Threatsmentioning

confidence: 99%

Moving Beyond Benchmarks and Competitions: Towards Addressing Social Media Challenges in an Educational Context

et al. 2023

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Natural language processing and other areas of artificial intelligence have seen staggering progress in recent years, yet much of this is reported with reference to somewhat limited benchmark datasets.We see the deployment of these techniques in realistic use cases as the next step in this development. In particular, much progress is still needed in educational settings, which can strongly improve users’ safety on social media. We present our efforts to develop multi-modal machine learning algorithms to be integrated into a social media companion aimed at supporting and educating users in dealing with fake news and other social media threats.Inside the companion environment, such algorithms can automatically assess and enable users to contextualize different aspects of their social media experience. They can estimate and display different characteristics of content in supported users’ feeds, such as ‘fakeness’ and ‘sentiment’, and suggest related alternatives to enrich users’ perspectives. In addition, they can evaluate the opinions, attitudes, and neighbourhoods of the users and of those appearing in their feeds. The aim of the latter process is to raise users’ awareness and resilience to filter bubbles and echo chambers, which are almost unnoticeable and rarely understood phenomena that may affect users’ information intake unconsciously and are unexpectedly widespread.The social media environment is rapidly changing and complex. While our algorithms show state-of-the-art performance, they rely on task-specific datasets, and their reliability may decrease over time and be limited against novel threats. The negative impact of these limits may be exasperated by users’ over-reliance on algorithmic tools.Therefore, companion algorithms and educational activities are meant to increase users’ awareness of social media threats while exposing the limits of such algorithms. This will also provide an educational example of the limits affecting the machine-learning components of social media platforms.We aim to devise, implement and test the impact of the companion and connected educational activities in acquiring and supporting conscientious and autonomous social media usage.

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Section: Content-based Threatsmentioning

confidence: 99%

Moving Beyond Benchmarks and Competitions: Towards Addressing Social Media Challenges in an Educational Context

et al. 2023

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“…DeepFakes are a set of DL based techniques allowing to create fake videos by swapping the face of a person by that of another. This technology has many diverse applications such as expression re-enactment ( Bansal et al, 2018 ) or video de-identification ( Bursic et al, 2021 ). However, in recent years the quality of deepfakes has reached tremendous levels of realism, thus posing a series of treats related to the possibility of arbitrary manipulation of identity, such as political propaganda, blackmailing, and fake news ( Mirsky & Lee, 2021 ).…”

Section: Case Study: Deepfake Detection With Pyvhrmentioning

confidence: 99%

pyVHR: a Python framework for remote photoplethysmography

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et al. 2022

PeerJ Computer Science

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Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood volume pulse (BVP) signal upon which BPMs (Beats per Minute) can be estimated. Furthermore, learning-based rPPG methods have been recently proposed. The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations. It is designed for both theoretical studies and practical applications in contexts where wearable sensors are inconvenient to use. Namely, pyVHR supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets. It is built up on accelerated Python libraries for video and signal processing as well as equipped with parallel/accelerated ad-hoc procedures paving the way to online processing on a GPU. The whole accelerated process can be safely run in real-time for 30 fps HD videos with an average speedup of around 5. This paper is shaped in the form of a gentle tutorial presentation of the framework.

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“…Remote-PPG measures blood flow changes by analysing the variations of the skin-reflected light captured through a general-purpose RGB-camera placed in front of the person [ 7 ]. While rPPG widens the scope of applications [ 8 , 9 , 10 ], its utility clearly depends on its reliability. As with contact-PPG signals, remotely estimated PPG waveforms carry respiratory-related information; there exists at least three different kinds of Respiratory Induced Variations, RIVs that can be eventually mined from (r)PPG signals: (cfr.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation

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D’Amelio

Ghezzi

et al. 2023

Sensors

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The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.

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A Quantitative Evaluation Framework of Video De-Identification Methods

Cited by 6 publications

References 27 publications

Moving Beyond Benchmarks and Competitions: Towards Addressing Social Media Challenges in an Educational Context

Moving Beyond Benchmarks and Competitions: Towards Addressing Social Media Challenges in an Educational Context

pyVHR: a Python framework for remote photoplethysmography

An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation

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