FLIDS: Fuzzy Logic-based Framework for Interpretable Image Manipulation Detection
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 work introduces FLIDS (Fuzzy Logic-based Image Distortion Scoring), an interpretable and efficient system for image tampering detection based on hand-crafted features and fuzzy logic. FLIDS combines JPEG artifact analysis, edge consistency, co-occurrence entropy, and CFA disparities into a fuzzy rule-based system for assigning a tampering confidence score. In contrast to black-box deep learning systems, FLIDS prioritizes transparency and generalizability. Tests on CIFAR-10, MNIST, ImageNet Subset, and Deepfake datasets indicate FLIDS attains competitive accuracy compared to ResNet-18, Autoencoder, and hand-designed JPEG detectors in the majority of instances. FLIDS achieves 93.5% and 91.8% accuracy on CIFAR-10 and ImageNet Subset, respectively, as well as a balanced 90.2% on deepfake datasets. These findings point to FLIDS as a promising, interpretable solution to intricate deep learning systems in image forgery 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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