Context-Aware Location De-Identification Using Denoising Diffusion
Bibliographic record
Abstract
In an era of increasing digital privacy risks, images shared online can inadvertently reveal sensitive location data through identifiable elements such as logos, road signs, and text. These disclosures enable unauthorized tracking and data mining, raising serious privacy concerns. This paper proposes a novel framework that combines object detection and generative inpainting for privacy preserving image reconstruction. A Mask R-CNN model is trained on three diverse datasets to detect and segment location identifiable elements accurately. The detected regions are then de-identified using a proposed Denoising Diffusion Probabilistic Model (DDPM)-based inpainting method, which preserves scene integrity by ensuring geometric consistency and natural lighting. Unlike traditional inpainting methods, the proposed framework dynamically refines image reconstructions through controlled denoising, achieving high realism. The effectiveness of the method is evaluated using standard image quality metrics, including PSNR, SSIM, and FID, alongside subjective visual assessments. Experimental results show that the proposed approach outperforms baseline models such as CNNs and GANs, offering a robust solution for privacy preserving image reconstruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".