Blur filtration fails to preserve privacy for home-based video conferencing

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Defeating Image Obfuscation with Deep Learning (2016, background): “Neustaedter et al. [30] analyzed how obfuscation can balance privacy with awareness in always-on remote video situations, where users would like to recognize the remote person without being able to see the privacy-sensitive parts of the video.”
Model-Based Face De-Identification (2006, methodology): “While some of these techniques have been shown to reduce the capability of human observers to identify people or actions in the scene [7, 20, 27], no formal privacy guarantees are made.”
Handbook of Face Recognition, 2nd Edition (2011, background): “In several papers Sweeney and collaborators [36, 23, 25, 24] have described a series of al-”
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, unknown): “Apart from detecting attributes, some works introduce new privacy challenges in vision such as adversarial perturbations [31, 35], privacy-preserving video capture [1, 36, 33, 37], person re-identification [2, 30], avoiding face detection [46, 16], full body re-identification [34] and privacy-sensit”
Getting the Face Behind the Squares: Reconstructing Pixelized Video Streams (2011, background): “raise some concerns when blurring and pixelization are used for video-conferencing in home settings [10].”