Deepfake geography: a novel detection method for identifying manipulated satellite images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.000 |
| 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 it