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<p>Industrial visual monitoring systems increasingly support process awareness, remote inspection, and safety–critical alarming. However, visually plausible tampering may evade conventional image-forensics methods when local texture, noise, and boundary artifacts are weak, while still contradicting synchronized equipment states or sensor readings. We propose PSR-Net, a process-state consistency verification framework for industrial image tampering detection and localization. PSR-Net encodes image-region features and synchronized process-state vectors into a shared representation space, uses a process-state re-indexing module to estimate region-level process relevance, and reconstructs the visual representation expected under the current operating condition. The residual between actual and reconstructed region features is then used as evidence for image-level attack detection and region-level tampering localization. We construct ICS-Tamper, a process-synchronized industrial image tampering dataset containing 13,500 image-state sample groups, including 8,100 normal samples and 5,400 tampered samples from six tampering types. On the ICS-Tamper test set, PSR-Net achieves 94.08% Precision, 92.74% Recall, and 93.41% F1-score, improving over the strongest baseline by 3.77, 4.12, and 3.95 percentage points, respectively. Ablation, process-inconsistent subset analysis, complexity comparison, and visualization results support the roles of process-state re-indexing and residual reconstruction. Our code is publicly available at <a href="https://github.com/cdt-zhangwei">https://github.com/cdt-zhangwei</a>.</p>

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PSR-Net: process-state consistency verification for industrial image tampering detection and localization

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Jifan Li,
  • Bin Lu,
  • Wei Zhang

摘要

Industrial visual monitoring systems increasingly support process awareness, remote inspection, and safety–critical alarming. However, visually plausible tampering may evade conventional image-forensics methods when local texture, noise, and boundary artifacts are weak, while still contradicting synchronized equipment states or sensor readings. We propose PSR-Net, a process-state consistency verification framework for industrial image tampering detection and localization. PSR-Net encodes image-region features and synchronized process-state vectors into a shared representation space, uses a process-state re-indexing module to estimate region-level process relevance, and reconstructs the visual representation expected under the current operating condition. The residual between actual and reconstructed region features is then used as evidence for image-level attack detection and region-level tampering localization. We construct ICS-Tamper, a process-synchronized industrial image tampering dataset containing 13,500 image-state sample groups, including 8,100 normal samples and 5,400 tampered samples from six tampering types. On the ICS-Tamper test set, PSR-Net achieves 94.08% Precision, 92.74% Recall, and 93.41% F1-score, improving over the strongest baseline by 3.77, 4.12, and 3.95 percentage points, respectively. Ablation, process-inconsistent subset analysis, complexity comparison, and visualization results support the roles of process-state re-indexing and residual reconstruction. Our code is publicly available at https://github.com/cdt-zhangwei.