Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging
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
The Intelligence, Surveillance, and Reconnaissance (ISR) community relies heavily on the use of overhead imagery for object detection and classification. In these applications, machine learning frameworks have been increasingly used to assist analysts in distinguishing high value targets from mundane objects quickly and effectively. In recent years, the robustness of these frameworks has come under question due to the possibility for disruption using image-based adversarial attacks, and as such, it is necessary to harden existing models against these threats. In this work, we survey a collection of three techniques to address these concerns at various stages of the image processing pipeline: external validation using Activity Based Intelligence, internal validation using Latent Space Analysis, and adversarial prevention using biologically inspired techniques. We found that biologically-inspired techniques were most effective and generalizable for mitigating adversarial attacks on overhead imagery in machine learning frameworks, with improvements as much as 34.6% over traditional augmentations, and 80.4% over a model without any augmentation-based defense.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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