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Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images
[article]
2023
arXiv
pre-print
Preserved Fulltext
Based on the results, we proposed a method for modeling the trade-off of privacy preservation and activity recognition on low-resolution images. ...
Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors. ...
performance on the main activity recognition task and visual privacy awareness. 2) We presented a model for calculating the trade-off of visual privacy preserving activity recognition using low-resolution ...
arXiv:2303.10435v1
fatcat:vbj7icunorapvazac2sgdotlie
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
[chapter]
2018
Lecture Notes in Computer Science
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The proposed framework explicitly learns a degradation transform for the original video inputs, in order to optimize the trade-off between target task performance and the associated privacy budgets on ...
Two experiments on privacy-preserving action recognition, with privacy budgets defined in various ways, manifest the compelling effectiveness of the proposed framework in simultaneously maintaining high ...
Fig. 1 : 1 Basic adversarial training framework for privacy-preserving visual recognition.
Fig. 2 : 2 Target and Budget Task Performance Trade-off on SBU Dataset.
(smaller A N b ). ...
doi:10.1007/978-3-030-01270-0_37
fatcat:szih5jfeazdf3njeax7smbkteu
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
[article]
2020
arXiv
pre-print
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The proposed framework explicitly learns a degradation transform for the original video inputs, in order to optimize the trade-off between target task performance and the associated privacy budgets on ...
Two experiments on privacy-preserving action recognition, with privacy budgets defined in various ways, manifest the compelling effectiveness of the proposed framework in simultaneously maintaining high ...
Fig. 1 : 1 Basic adversarial training framework for privacy-preserving visual recognition.
Fig. 2 : 2 Target and Budget Task Performance Trade-off on SBU Dataset. ...
arXiv:1807.08379v2
fatcat:6sfqfymyvvgndec22cmc3o47wi
Privacy-Preserving Action Recognition via Motion Difference Quantization
[article]
2022
arXiv
pre-print
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Towards this direction, this paper proposes a simple, yet robust privacy-preserving encoder called BDQ for the task of privacy-preserving human action recognition that is composed of three modules: Blur ...
Our experiments on three benchmark datasets show that the proposed encoder design can achieve state-of-the-art trade-off when compared with previous works. ...
-We show that the BDQ encoder achieves state-of-the-art trade-off between action recognition and privacy preservation on three benchmark datasets SBU, KTH, and IPN, when compared with other privacy-preserving ...
arXiv:2208.02459v1
fatcat:gsdv36hbmncutkscoeoeatzv7i
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens
[article]
2022
arXiv
pre-print
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The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. ...
activity recognition. ...
(a) Trade-off between privacy protection and action recognition on PA-HMDB51. ...
arXiv:2206.03891v1
fatcat:jawwl7qxhjfc5aa3pzcfbilhta
SPAct: Self-supervised Privacy Preservation for Action Recognition
[article]
2022
arXiv
pre-print
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Employing existing protocols of known-action and privacy attributes, our framework achieves a competitive action-privacy trade-off to the existing state-of-the-art supervised methods. ...
Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. ...
Acknowledgments We thank Vishesh Kumar Tanvar, Tushar Sangam, Rohit Gupta, and Zhenyu Wu for constructive suggestions. ...
arXiv:2203.15205v1
fatcat:lhbaihhsqfcqfiubsxxqvpypoe
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
[article]
2021
arXiv
pre-print
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A novel adversarial training framework is formulated to learn an anonymization transform for input videos such that the trade-off between target utility task performance and the associated privacy budgets ...
We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our ...
The authors would like to sincerely thank Scott Hoang, James Ault, and Prateek Shroff for assisting the labeling of the PA-HMDB51 dataset. ...
arXiv:1906.05675v6
fatcat:prf4wzjbufgvneeaazwet77ib4
A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework
2021
Bulletin of Electrical Engineering and Informatics
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There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. ...
Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. ...
[3] investigated on the trade-off between performance of action recognition while preserving privacy and number of cameras and their resolution. ...
doi:10.11591/eei.v10i6.3204
fatcat:xs64vmm7urhjdighpc23pmtgky
Performance of video processing at the edge for crowd-monitoring applications
2018
2018 IEEE 4th World Forum on Internet of Things (WF-IoT)
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In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on lowpowered gateway devices. ...
We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and rescaling video frames can optimise ...
ACKNOWLEDGMENT The authors would like to thank Intel Ireland for providing the gateway device. ...
doi:10.1109/wf-iot.2018.8355170
dblp:conf/wf-iot/BallasMZOL18
fatcat:yjq2k7dxivbmvip2g3e3h7i3m4
Face-Off: Adversarial Face Obfuscation
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2003.08861v1 fatcat:6f5dxeqygfgmfnyjwipuqcwao4
To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. ...
In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to the user's face to prevent it from being correctly recognized. ...
The observations from our user study show that Face-Off improves on the privacy-utility trade-off of users. ...
arXiv:2003.08861v2
fatcat:m5q3gdbjyvgprioef5ozh5noku
Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy
[article]
2022
arXiv
pre-print
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With rapid advancements in image generation technology, face swapping for privacy protection has emerged as an active area of research. ...
Through this design, we are the first to provide a methodology for assessing the privacy-utility trade-offs for the face swapping approach to patient privacy protection. ...
Acknowledgments This work was was supported by the National Institute of Mental Health through award R21MH123997. We thank Dr. ...
arXiv:2204.03559v2
fatcat:jcns2a7vvzahpepwl7cpdu6a2u
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
[article]
2022
arXiv
pre-print
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Previous works simply mask most areas of faces or synthesize samples using generative models to construct privacy-preserving face datasets, which overlooks the trade-off between privacy protection and ...
Experiments prove that the identities of reconstructed images are difficult to be retrieved. We also perform sufficient privacy-preserving face recognition on several public face datasets (i.e. ...
We thank TACC (Texas Advanced Computing Center) for supporting us to get access to the Longhorn supercomputer and the Frontera supercomputer. ...
arXiv:2205.11090v1
fatcat:x5ygfsx4eralrmkf7tyzhgjfwa
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition
[article]
2020
arXiv
pre-print
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Yet, due to the strong privacy concerns, the use of such technologies is met with strong skepticism, since current face-based emotion recognition systems relying on deep learning techniques tend to preserve ...
Results indicate that the proposed approach can learn a convolutional transformation for preserving emotion recognition accuracy and degrading face identity recognition, providing a foundation toward privacy-aware ...
In the light of these, prior work has approached the problem of privacy preservation in general human activity recognition from two different approaches, one relying on pre-defined transformations of an ...
arXiv:2001.06103v1
fatcat:afvwxmcytfhmbot33of6thhwqe
Balancing Privacy Protection and Interpretability in Federated Learning
[article]
2023
arXiv
pre-print
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Finally, extensive experiments on both IID and Non-IID data demonstrate that the proposed ADP can achieve a good trade-off between privacy and interpretability in FL. ...
We also theoretically analyze the impact of gradient perturbation on the model interpretability. ...
Related Work We review the related work on privacy-preserving federated learning and the trade-offs between privacy protection, accuracy, and interpretability. ...
arXiv:2302.08044v1
fatcat:hv636vwthrdutibu77kdnkvnuu
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2403.10558v1 fatcat:lrswqh76oncpvlvvmjzrtungf4
To overcome this trade-off, we develop an enhanced adaptive MixUp strategy based on reinforcement learning, which enables us to mix a larger number of images while maintaining satisfactory recognition ...
Previous studies have shown that increasing the number of images mixed in MixUp can enhance privacy preservation but at the expense of reduced face recognition accuracy. ...
Finally, the two reached a better trade-off between privacy and utility. ...
arXiv:2403.10558v2
fatcat:no6xlt2uezdira6gjsug6ch6cy
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