PulseEdit: Editing Physiological Signals in Facial Videos for Privacy Protection

Chen, Mingliang; Liao, Xin; Wu, Min

doi:10.1109/tifs.2022.3142993

Cited by 33 publications

(12 citation statements)

References 50 publications

(84 reference statements)

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“…Comparison with PulseEdit: Table I shows the MAE and PSNR for the proposed method with λ = 0.1 and PulseEdit with their default parameter on both PURE and MMSE-HR datasets with full faces or partial faces. The results show that the proposed method has a lower MAE, which indicates that the modification of the proposed method is more effective than that of PulseEdit [22]. The MAE values of PulseEdit on deep learning-based rPPG methods are much higher than traditional rPPG methods, while the proposed method can keep low MAE on both deep learning-based and traditional rPPG methods.…”

Section: B Experimental Resultsmentioning

confidence: 90%

“…These videos are divided into 15-sec video clips, and we use the proposed method to embed a 15-sec target PPG signal into these video clips. Similar to PulseEdit [22], we generate a sinusoid of frequency 120 beats per minute (bpm) as the target rPPG signal. For the parameters in the proposed method, we try different values of λ and report the results in the following.…”

Section: A Experiments Setupmentioning

confidence: 99%

“…Two papers [21], [22] explored to conceal rPPG in facial videos for privacy protection. Chen et al [21] used Laplacian pyramid representation to remove the color changes induced by rPPG signals in facial videos.…”

Section: Introductionmentioning

confidence: 99%

“…(e-mail: zhaodong.sun@oulu.fi; xiaobai.li@oulu.fi) (Corresponding author: Xiaobai Li) was designed to eliminate the rPPG, which might produce unrealistic videos as mentioned by [23]. Recently, PulseEdit [22] was proposed to modify rPPG in facial videos. They generated a target rPPG signal and solved an optimization problem with the target and original rPPG signals to get the perturbation.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Phys: Facial Video-Based Physiological Modification for Privacy Protection

Sun

2022

IEEE Signal Process. Lett.

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The invisible remote photoplethysmography (rPPG) signals in facial videos can reveal the cardiac rhythm and physiological status. Recent studies show that rPPG is a noncontact way for emotion recognition, disease detection, and biometric identification, which means there is a potential privacy problem about physiological information leakage from facial videos. Therefore, it is essential to process facial videos to prevent rPPG extraction in privacy-sensitive situations such as online video meetings. In this letter, we propose Privacy-Phys, a novel method based on a pre-trained 3D convolutional neural network, to modify rPPG in facial videos for privacy protection. Our experimental results show that our approach can modify rPPG signals in facial videos more effectively and efficiently than the previous baseline. Our method can be applied to process facial videos in online video meetings or video-sharing platforms to prevent rPPG from being captured maliciously.

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Section: B Experimental Resultsmentioning

confidence: 90%

Section: A Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Privacy-Phys: Facial Video-Based Physiological Modification for Privacy Protection

Sun

2022

IEEE Signal Process. Lett.

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“…5 A large number of studies in our community have been focused on the protection (e.g. privacy, 6 security, 7 malicious behaviours) 8 of our lives, towards constructing a safer information environment. Shamir proposed the weighted threshold secret sharing scheme.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical secret sharing scheme for WSN based on linear homogeneous recurrence relations

Lin

Zhu

et al. 2022

International Journal of Distributed Sensor Networks

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Wireless sensor network is a key technology in the sensing layer of the Internet of Things. Data security in wireless sensor network is directly related to the authenticity and validity of data transmitted in the Internet of Things. Due to the large number and different types of nodes in wireless sensor networks, layered secret key sharing technology is increasingly used in wireless sensor networks. In a hierarchical secret sharing scheme, participants are divided into sections with different permissions for each team, but the same permissions for participants in the same team. In this article, we follow the approach of the hierarchical secret sharing scheme derived from the linear homogeneous recurrence relations. We design a hierarchical multi-secret sharing scheme for wireless sensor networks on the basis of the elliptic curve public key cryptosystem combined with the linear homogeneous recurrence relations. In the proposed scheme, we do not make sure that the participants are half-truthful. In addition, the participants’ shadows can be reused. Our scheme is computational security. Only one share from each member is required in our hierarchical multi-secret sharing scheme. It is more suitable for wireless sensor networks compared to the up-to-date schemes.

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Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Zhang

et al. 2023

Advanced Intelligent Systems

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In recent years, optical imaging techniques have gained wide recognition for the measurement of vital signals, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, which are crucial indicators for evaluating human health conditions in clinical examinations. There is a wide range of optical imaging methods for remote physiological signal monitoring, including RGB imaging, thermal imaging, hyperspectral imaging, depth imaging, and multimodal imaging, which provide spatial information compared to other noncontact measurement approaches, thereby allowing extensive applications in this area. In this survey, some fundamental knowledge about optical imaging methods for vital signal measurement is reviewed, including principles of various optical imaging techniques, processing methods for data analysis, discussion on advantages and disadvantages, application summary, and future prospects. This is a comprehensive overview of the noncontact physiological signal measurement of optical imaging approaches.

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PulseEdit: Editing Physiological Signals in Facial Videos for Privacy Protection

Cited by 33 publications

References 50 publications

Privacy-Phys: Facial Video-Based Physiological Modification for Privacy Protection

Privacy-Phys: Facial Video-Based Physiological Modification for Privacy Protection

Hierarchical secret sharing scheme for WSN based on linear homogeneous recurrence relations

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

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