Subtle periodic signals such as blood volume pulse and respiration can be
extracted from RGB video, enabling remote health monitoring at low cost.
Advancements in remote pulse estimation -- or remote photoplethysmography
(rPPG) -- are currently driven by deep learning solutions. However, modern
approaches are trained and evaluated on benchmark datasets with associated
ground truth from contact-PPG sensors. We present the first non-contrastive
unsupervised learning framework for signal regression to break free from the
constraints of labelled video data. With minimal assumptions of periodicity and
finite bandwidth, our approach is capable of discovering the blood volume pulse
directly from unlabelled videos. We find that encouraging sparse power spectra
within normal physiological bandlimits and variance over batches of power
spectra is sufficient for learning visual features of periodic signals. We
perform the first experiments utilizing unlabelled video data not specifically
created for rPPG to train robust pulse rate estimators. Given the limited
inductive biases and impressive empirical results, the approach is
theoretically capable of discovering other periodic signals from video,
enabling multiple physiological measurements without the need for ground truth
signals. Codes to fully reproduce the experiments are made available along with
the paper.

应用无监督深度学习方法，可以从非标记视频中准确提取血容量脉动等生理周期信号，进而实现远程监控和多种生理测量，无需地面真实信号。

视频中生理信号的非对比非监督学习

Non-Contrastive Unsupervised Learning of Physiological Signals from  Video

Deepfake is a deep learning-based technique that makes it easy to change or
modify images and videos. In investigations and court, visual evidence is
commonly employed, but these pieces of evidence may now be suspect due to
technological advancements in deepfake. Deepfakes have been used to blackmail
individuals, plan terrorist attacks, disseminate false information, defame
individuals, and foment political turmoil. This study describes the history of
deepfake, its development and detection, and the challenges based on
physiological measurements such as eyebrow recognition, eye blinking detection,
eye movement detection, ear and mouth detection, and heartbeat detection. The
study also proposes a scope in this field and compares the different biological
features and their classifiers. Deepfakes are created using the generative
adversarial network (GANs) model, and were once easy to detect by humans due to
visible artifacts. However, as technology has advanced, deepfakes have become
highly indistinguishable from natural images, making it important to review
detection methods.

探讨 Deepfake 技术的历史、发展和检测，并议论了基于生理测量（如眉毛识别、眨眼检测、眼动检测、耳朵和口部检测、心跳检测等）的挑战，同时提出了各种不同的生物特征及分类器比较，并对 GANs 模型的检测方法进行了综述。

使用生物特征进行 Deepfake 检测：一项调查

Deepfake Detection using Biological Features: A Survey

Physiological measurements involves observing variables that attribute to the
normative functioning of human systems and subsystems directly or indirectly.
The measurements can be used to detect affective states of a person with aims
such as improving human-computer interactions. There are several methods of
collecting physiological data, but wearable sensors are a common, non-invasive
tool for accurate readings. However, valuable information is hard to extract
from the raw physiological data, especially for affective state detection.
Machine Learning techniques are used to detect the affective state of a person
through labeled physiological data. A clear problem with using labeled data is
creating accurate labels. An expert is needed to analyze a form of recording of
participants and mark sections with different states such as stress and calm.
While expensive, this method delivers a complete dataset with labeled data that
can be used in any number of supervised algorithms. An interesting question
arises from the expensive labeling: how can we reduce the cost while
maintaining high accuracy? Semi-Supervised learning (SSL) is a potential
solution to this problem. These algorithms allow for machine learning models to
be trained with only a small subset of labeled data (unlike unsupervised which
use no labels). They provide a way of avoiding expensive labeling. This paper
compares a fully supervised algorithm to a SSL on the public WESAD (Wearable
Stress and Affect Detection) Dataset for stress detection. This paper shows
that Semi-Supervised algorithms are a viable method for inexpensive affective
state detection systems with accurate results.

本文研究了使用可穿戴传感器收集生理数据来探测情感状态的方法，指出了使用机器学习算法对已标记的生理数据进行情感状态检测的方法存在的问题，提出了使用半监督学习算法来减少标记成本的解决方案，并通过在公开数据集 WESAD 进行实验比较了半监督算法和全监督算法的性能，结果表明半监督算法是一种价格低廉且具有准确性的检测情感状态的有效方法。