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ProCoS: Degradation-Robust Manipulation Localization for Special Equipment Inspection Images.

Guilong Chen¹, Guixiong Liu¹, Weili Luo²

¹School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China.

Sensors (Basel, Switzerland)

|June 26, 2026

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This study introduces the Prototypical Contrastive-Synergistic Network (ProCoS) to improve image manipulation localization in Special Equipment Inspection (SEI) images. ProCoS enhances reliable detection under complex degradations, crucial for industrial supervision.

Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Accurate manipulation localization in Special Equipment Inspection (SEI) images is vital for trustworthy industrial supervision.
Existing models falter with compound degradation disturbances common in SEI scenarios.

Purpose of the Study:

To develop an advanced model for robust image manipulation localization in SEI under compound degradations.
To introduce a new dataset and evaluation protocol for SEI manipulation localization.

Main Methods:

Proposed the Prototypical Contrastive-Synergistic Network (ProCoS) incorporating Degradation-Resilient Synergistic Learning.
Implemented a Prototypical Contrastive Learning mechanism for stable representations under compound degradation.
Constructed the SEI-Asym dataset and an orthogonal experimental design-based compound degradation protocol.

Keywords:

degradation robustness degradation-resilient synergistic learning image manipulation localization prototypical contrastive learning special equipment inspection

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Main Results:

ProCoS achieved F1 of 0.6531, AUC of 0.9670, and IoU of 0.5485 on the SEI-Asym dataset.
Reduced ΔF1¯ and (ΔF1)max under compound degradation to 0.0873 and 0.1966, respectively.
Demonstrated superior performance in handling compound degradation disturbances.

Conclusions:

The Prototypical Contrastive-Synergistic Network (ProCoS) offers an effective solution for image manipulation localization in SEI.
The proposed model enhances trustworthy perception, anomaly discrimination, and industrial supervision using SEI images.