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QIF-T0098

critical

WiFi + camera passive surveillance fusion (through-wall presence detection with visual identification)

Tier 4 — Demonstrated (Case Study / Observational)

Legacy status: EMERGING

WiFi CSI (T0090) provides through-wall presence detection, vital signs, and coarse pose estimation but cannot visually identify targets. Cameras (T0084) provide visual identification and remote PPG but require line of sight. By fusing WiFi CSI and camera data, an attacker achieves persistent surveillance that combines the strengths of both: WiFi tracks targets through walls and identifies them by body shape/gait, while cameras provide visual identification when line of sight is available. The fusion enables: handoff tracking (camera identifies person entering a building, WiFi CSI tracks them inside), activity recognition (WiFi CSI classifies activity, camera confirms), and vital sign correlation (WiFi breathing rate + camera heart rate). Zhao et al. (2018) demonstrated that WiFi signals alone can reconstruct 2D human poses comparable to visual skeleton tracking. This creates a surveillance system that requires no devices on the target and works through physical barriers.

Technique Details

Tactic
QIF-S.CH
Status
EMERGING
Bands
S1, S2, S3

Therapeutic Application

Fusion of WiFi CSI through-wall sensing with camera-based visual identification for persistent surveillance that works through physical barriers

Clinical Analog

Ambient assisted living for elderly monitoring

Treats

  • elderly fall detection (through-wall + visual confirmation)
  • dementia patient monitoring in care facilities
  • post-surgical recovery activity monitoring

Neural Impact

3 of 7 neural bands affected

S1 S2 S3

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Scoring

NISS v1.1 NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N
CVSS v4.0 CVSS:4.0/AV:A/AC:L/AT:P/PR:H/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N
2.0Low
BICRCDCVRVNP
 

Governance

Neurorights at Risk

This technique threatens 2 of the 4 proposed neurorights (Ienca & Andorno, 2017).

Consent Complexity
0.90 / 4.0

FDORA §3305 Compliance

Cyber Device
Regulatory Coverage
0.4 / 1.0
524B Requirements
TM VA SBOM SA PM
Regulatory Gaps
  • ! CVSS partially captures risk; neural dimensions missing
  • ! No FDA pathway for consumer sensor exploitation

Population Vulnerability

CRB vulnerability adjustment (γ=0.30) accounts for age, diagnosis severity, consent capacity, and device dependency.

Population NISS Base Adjusted Severity Delta
Adult (Default) 2.0 2.0 Low -
Child (10yr) + ADHD 2.0 2.4 Low +0.35
Adult with ALS 2.0 2.3 Low +0.32

Validation Status

Theoretical / Not yet validated. This technique has not been independently tested. See the validation dashboard for what has been tested.

Qinnovate Neural Security Atlas Edit this on GitHub