# Tracking without Seeing: Geospatial Inference using Encrypted Traffic from Distributed Nodes

arXiv: https://arxiv.org/abs/2603.27811 · HTML: https://mlsanity.com/paper/2603.27811/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 29 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://mlsanity.com/paper/1912.06613/) (2019)
- [MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT](https://mlsanity.com/paper/2411.12126/) (2024)
- [Heteroskedastic Geospatial Tracking with Distributed Camera Networks](https://mlsanity.com/paper/2306.02407/) (2023)
- [I Always Feel Like Somebody's Sensing Me! A Framework to Detect, Identify, and Localize Clandestine Wireless Sensors](https://mlsanity.com/paper/2005.03068/) (2020)

## Built on by (most cited first, 2 of 2)

- [Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable](https://mlsanity.com/paper/2605.10404/) (2026)
- [Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking](https://mlsanity.com/paper/2609.18173/) (2026)

## Abstract (arXiv)

Accurate observation of dynamic environments traditionally relies on synthesizing raw, signal-level information from multiple distributed sensors. This work investigates an alternative approach: performing geospatial inference using only encrypted packet-level information, without access to the raw sensory data. We further explore how this indirect information can be fused with directly available sensory data to extend overall inference capabilities. We introduce GraySense, a learning-based framework that performs geospatial object tracking by analyzing encrypted wireless video transmission traffic, such as packet sizes, from cameras with inaccessible streams. GraySense leverages the inherent relationship between scene dynamics and transmitted packet sizes to infer object motion. The framework consists of two stages: (1) a Packet Grouping module that identifies frame boundaries and estimates frame sizes from encrypted network traffic, and (2) a Tracker module, based on a Transformer encoder with a recurrent state, which fuses indirect packet-based inputs with optional direct camera-based inputs to estimate the object's position. Extensive experiments with realistic videos from the CARLA simulator and emulated networks under varying conditions show that GraySense achieves 2.33 meters tracking error (Euclidean distance) without raw signal access, within the dimensions of tracked objects (4.61m x 1.93m). To our knowledge, this capability has not been previously demonstrated, expanding the use of latent signals for sensing.