Deepfake geography: a novel detection method for identifying manipulated satellite images
Bibliographic record
Abstract
This paper explores the field of deepfake geography, which involves the creation and detection of manipulated satellite images using advanced deep-learning techniques. It begins with a discussion of the increasing accessibility and realism of deepfake technology, as well as its potential impact on trust, public opinion, and the dissemination of disinformation. Then, the manipulation of maps and geographical information is examined, highlighting notable examples and recent advancements in generative AI for creating synthetic satellite imagery. While prior studies have explored the detection of synthetic satellite images, they do not address the more challenging task of identifying manipulated content within real geospatial data. To fill this gap, a new deep-learning-based detection method is introduced, and its performance is evaluated using a dataset of deepfake-geography images created with a state-of-the-art generative model. The results demonstrate the effectiveness of the proposed method in detecting fake areas in real satellite images. The paper concludes by discussing the implications of its findings and suggesting potential avenues for future research in deepfake-geography creation and detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".