CoChat
Sign Up
Cited by 18Chaos-based Image/Signal EncryptionCryptography and Data SecurityCryptographic Implementations and Security

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Open access
Du, Bowen, Li, Weiqi, Wang, Zeju, Xu, Manxin, Gao, Tianchen, Li, Jiajie, Wen, Hongkai·2021·Sensors
DOI 10.3390/s21134320PMC PMC8272045
Open this paper in CoChat — free accountOpen PDF

Abstract

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s21134320

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The encryption framework of Du et al. [12] is incompatible with direct execution of downstream computer vision tasks; encrypted event streams require decryption before practical application.

    “However, the limitation of this encryption approach is the incompatibility with the direct execution of downstream computer vision tasks. The encrypted event stream primarily prevents privacy attacks during transmission or storage, requiring decryption prior to any practical application.”

    From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [12]

    Section 5.1 reports that images reconstructed directly from encrypted events were nearly all dark, and Section 5.2 reports that identification accuracy on encrypted events fell to random-guess levels. Section 4.2 describes how to decrypt events, but the paper does not establish that every downstream computer-vision task is incompatible with encrypted streams or that decryption is required before any practical application. Page numbers are unavailable in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Du et al. [12] introduced a manually designed encryption framework that uses spatial chaotic mapping to scramble event positions and invert their polarities.

    “Du et al. [12] recently introduced a manually designed encryption framework to counteract privacy attacks on event data. This method uses spatial chaotic mapping to scramble event positions and invert their polarities.”

    From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [12]

  • The proposed event anonymization method outperforms the event encryption technique.

    “Our proposed event anonymization method outperforms the event encryption technique.”

    From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [12]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. [8]Qiao L. Nahrstedt K. A new algorithm for MPEG video encryption Proceedings of the First International Conference on Imaging Science System and Technology Las Vegas, NV, USA 30 June 30–3 July 1997 21 29
  2. [10]Wu C.P. Kuo C.C.J. Efficient multimedia encryption via entropy codec design Proceedings of the Photonics West 2001—Electronic Imaging San Jose, CA, USA 20 January 2001 Volume 4314 128 138
  3. [16]Lenz G. Ieng S.H. Benosman R. Event-based face detection and tracking in the blink of an eye arXiv 2018 1803.10106 PMC7397845 32848527

Cited by

Sorted by citations

Related

Authors
Du, B., Li, W., Wang, Z., Xu, M., Gao, T., Li, J., & Wen, H.
Affiliations
  • University of Warwick (GB)
  • Tongji University (CN)
Corresponding author
Wen, Hongkai
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-06-24
Volume
21
Issue
13
Pages
4320
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21134320
PMC
PMC8272045
OpenAlex
W3177306017
Cited by
18
References
38
Citations by year
2026: 1 · 2025: 6 · 2024: 4 · 2023: 5 · 2022: 2
FWCI
0.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
Keywords
neuromorphic vision sensors, privacy preservation, resource-constrained devices, video surveillance security
MeSH terms
Algorithms, Humans, Computer Security, Privacy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review