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TSLEPS: A Two-Stage Localization and Erasure Method for Privacy Protection in Sensor-Captured Images.

Xiaoxu Li^1,2, Jun Fu³, Jinjian Wang^1,2

¹Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alar 843300, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces a Two-Stage Localization and Erasure method for Privacy protection in Sensor-captured images (TSLEPS). TSLEPS effectively detects and removes private text from images, preserving visual quality for mobile devices.

Keywords:

image sensors object detection privacy protection privacy text erasure visual privacy

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Area of Science:

Computer Vision
Image Processing
Cybersecurity

Background:

Increasing use of mobile imaging sensors raises concerns about image privacy leakage.
Current manual privacy protection methods are inefficient and degrade image quality.

Purpose of the Study:

To develop an automated and efficient method for protecting sensitive text information in sensor-captured images.
To maintain image integrity and usability after privacy protection.

Main Methods:

Proposed TSLEPS (Two-Stage Localization and Erasure method for Privacy protection in Sensor-captured images) with a two-stage framework.
Utilized an inverted residual attention mechanism and generalized efficient aggregation layer for target detection.
Employed a texture-enhanced feature attention mechanism and adversarial generative network for text erasure.
Incorporated half-instance normalization for computational efficiency on mobile devices.

Main Results:

Achieved 97.5% accuracy and 96.4% recall in privacy target detection.
Reached 38.2140 dB PSNR and 0.9607 SSIM for privacy text erasure quality.
Demonstrated effective performance on public real-world privacy datasets.

Conclusions:

TSLEPS offers an effective solution for privacy protection in sensor-captured images.
The method significantly improves detection accuracy, erasure quality, and computational efficiency.
TSLEPS is suitable for deployment on resource-constrained mobile devices.