# Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

arXiv: https://arxiv.org/abs/2605.00218 · HTML: https://mlsanity.com/paper/2605.00218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [Multivariate Time Series Classification with WEASEL+MUSE](https://mlsanity.com/paper/1711.11343/) (2017)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era](https://mlsanity.com/paper/2202.10673/) (2022)
- [Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing](https://mlsanity.com/paper/2310.13715/) (2023)
- [Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets](https://mlsanity.com/paper/2109.13514/) (2021)
- [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023)
- [Continuous Authentication of Smartphones Based on Application Usage](https://mlsanity.com/paper/1808.03319/) (2018)
- [A large-scale study of performance and equity of commercial remote identity verification technologies across demographics](https://mlsanity.com/paper/2409.12318/) (2024)
- [Review of Face Presentation Attack Detection Competitions](https://mlsanity.com/paper/2112.11290/) (2021)
- [Contrastive Learning for Continuous Touch-Based Authentication](https://mlsanity.com/paper/2504.17271/) (2025)

## Built on by (most cited first, 1 of 1)

- [Handwritten Digit Leakage from Smartphone Motion Sensors Across Unseen Users and Phone Models](https://mlsanity.com/paper/2609.32117/) (2026)

## Abstract (arXiv)

Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation attacks, real-time deepfakes, and video injection. Recent European requirements, including ETSI TS 119 461 and CEN/TS 18099, motivate evidence channels beyond camera-based presentation-attack detection. This paper studies whether passive motion traces recorded during selfie capture provide auxiliary evidence for spoof screening and user verification. We introduce CanSelfie, a dataset of 375 bona fide multi-sensor sequences collected at 50 Hz from 30 participants using a commercial mobile RIdV application, together with stationary, handheld, and temporally shifted attack-proxy scenarios. We benchmark 7 multivariate time-series classifiers and 8 whole-series anomaly detectors across sensor configurations and temporal windows. For spoof screening, accelerometer-only ROCKAD obtains 0.00% FRR and 43.8% FAR, while QUANT+3-NN obtains the lowest FAR of 32.0% at 2.37% FRR; both reject all stationary proxies. For same-device and same-session user verification, WEASEL+MUSE reaches 1.07% EER using 9 channels. Raw acceleration is the most informative modality, and closed-set accuracy alone does not imply good verification because threshold calibration depends on score distributions. The findings support selfie-capture motion as a low-friction auxiliary signal, while leaving cross-device, cross-session, and real injection-attack evaluation as necessary next steps. Code and data available at: https://github.com/Ergzar/Selfie-Motion-AD-TSC.